Shape metric based scoring of wafer locations

ABSTRACT

Methods and systems for shape metric based scoring of wafer locations are provided. One method includes selecting shape based grouping (SBG) rules for at least two locations on a wafer. For one of the wafer locations, the selecting step includes modifying distances between geometric primitives in a design for the wafer with metrology data for the one location and determining metrical complexity (MC) scores for SBG rules associated with the geometric primitives in a field of view centered on the one location based on the distances. The selecting step also includes selecting one of the SBG rules for the one location based on the MC scores. The method also includes sorting the at least two locations on the wafer based on the SBG rule selected for the at least two locations.

BACKGROUND OF THE INVENTION 1 Field of the Invention

This invention generally relates to methods and systems for shape metricbased scoring of wafer locations.

2. Description of the Related Art

The following description and examples are not admitted to be prior artby virtue of their inclusion in this section.

Inspection processes are used at various steps during a semiconductormanufacturing process to detect defects on wafers to promote higheryield in the manufacturing process and thus higher profits. Inspectionhas always been an important part of fabricating semiconductor devices.However, as the dimensions of semiconductor devices decrease, inspectionbecomes even more important to the successful manufacture of acceptablesemiconductor devices because smaller defects can cause the devices tofail.

“Care areas” as they are commonly referred to in the art are areas on aspecimen that are of interest for inspection purposes. Sometimes, careareas are used to differentiate areas on the specimen that are inspectedfrom areas on the specimen that are not inspected in an inspectionprocess. In addition, care areas are sometimes used to differentiatebetween areas on the specimen that are to be inspected with one or moredifferent parameters. For example, if a first area of a specimen is morecritical than a second area on the specimen, the first area may beinspected with a higher sensitivity than the second area so that defectsare detected in the first area with a higher sensitivity. Otherparameters of an inspection process can be altered from care area tocare area in a similar manner.

Different categories of inspection care areas are currently used. Onecategory is legacy care areas, which are traditionally hand drawn. Withnearly most users adopting design guided inspection, very few legacycare areas are currently used. Another category is design based careareas. These are care areas derived based on heuristics on chip designpatterns printed on the specimen. There are multiple techniques andtools available to define these design based care areas. As they arederived from ground truth (chip design), they end up providing highprecision, tiny care areas and also allow inspection systems to storehigh volumes of care areas. These care areas are important not just froma defect detection standpoint, but are often crucial to noisesuppression.

It may not always be straightforward to identify or select care areas ona specimen for inspection purposes. For example, the process forgenerating care areas may include running a substantially hotinspection, i.e., an inspection with an abnormally low threshold. Theevents detected by such an inspection may then be grouped based ondesign for the specimen proximate the events. Since the inspection isrun substantially hot, the detected events are more or less entirelynuisance. Therefore, based on the results of the design based groupingof the detected events, the portions of the design that generated themost frequently detected nuisance events may be identified. New careareas that contain these “nuisance generating” patterns may be created.It can however be difficult and/or time consuming to create these. Thesteps described above may be repeated until care areas are sufficientlygenerated.

Defect review typically involves re-detecting defects detected as suchby an inspection process and generating additional information about thedefects at a higher resolution using either a high magnification opticalsystem or a scanning electron microscope (SEM). Defect review istherefore performed at discrete locations on the wafer where defectshave been detected by inspection. The higher resolution data for thedefects generated by defect review is more suitable for determiningattributes of the defects such as profile, roughness, more accurate sizeinformation, etc.

Metrology processes are also used at various steps during asemiconductor manufacturing process to monitor and control the process.Metrology processes are different than inspection processes in that,unlike inspection processes in which defects are detected on a wafer,metrology processes are used to measure one or more characteristics ofthe wafer that cannot be determined using currently used inspectiontools. For example, metrology processes are used to measure one or morecharacteristics of a wafer such as a dimension (e.g., line width,thickness, etc.) of features formed on the wafer during a process suchthat the performance of the process can be determined from the one ormore characteristics. In addition, if the one or more characteristics ofthe wafer are unacceptable (e.g., out of a predetermined range for thecharacteristic(s)), the measurements of the one or more characteristicsof the wafer may be used to alter one or more parameters of the processsuch that additional wafers manufactured by the process have acceptablecharacteristic(s).

Metrology processes are also different than defect review processes inthat, unlike defect review processes in which defects that are detectedby inspection are re-visited in defect review, metrology processes maybe performed at locations at which no defect has been detected. In otherwords, unlike defect review, the locations at which a metrology processis performed on a wafer may be independent of the results of aninspection process performed on the wafer. In particular, the locationsat which a metrology process is performed may be selected independentlyof inspection results. In addition, since locations on the wafer atwhich metrology is performed may be selected independently of inspectionresults, unlike defect review in which the locations on the wafer atwhich defect review is to be performed cannot be determined until theinspection results for the wafer are generated and available for use,the locations at which the metrology process is performed may bedetermined before an inspection process has been performed on the wafer.

Huge benefits are provided by using design information to generate careareas and sample defects for review. One such advantage is thatinspections can be tailored to areas in the design that the user caresabout. Another advantage is that defects can be sampled based on thedesign so that additional information about defects that areparticularly relevant to the fabrication of the design on the wafer canbe prioritized. In one such example, defects can be sampled based on thepatterned features they are proximate to so that defects located in ornear high priority patterns can be sampled more heavily than otherdefects.

There are, however, a number of disadvantages for the currently usedmethods and systems for generating care areas and sampling defects forreview or other processes (e,g., metrology, etc.). For example,generating care areas and sampling defects based on design, in ofitself, does not necessarily take into consideration how the design isactually formed on the wafer. For example, the design as formed on thewafer will vary from the design as created in the design process (i.e.,the design intent). If the design is formed on the wafer with differentcharacteristics (e.g., dimensions, location of some patterned featuresrelative to other patterned features, shape, etc.) than designed, thosedifferent characteristics can change the complexity or priority of thepatterns (and hence the defects) on the wafer. Therefore, if the carearea generation and defect sampling processes do not take suchdifferences into consideration, the care areas and defect samples maynot adequately reflect the as-formed complexities and/or priorities ofthe patterns and defects on the wafers. In addition, such variations inthe characteristics of patterned features as designed compared to thepatterned features as formed may not necessarily be predictable. Forexample, simulations of how the design will be formed on the wafer maynot be able to accurately predict changes in the design as formed due toacross wafer variations, random variations, changes in processconditions, etc. Therefore, even if currently used methods and systemsgenerate care areas or defect sampling schemes based on a design and/orhow the design is expected to be formed on a wafer, those methods andsystems may still not be based on the actual complexities and/orpriorities of the patterned features formed on the wafer.

Accordingly, it would be advantageous to develop systems and/or methodsfor shape metric based sorting of wafer locations that do not have oneor more of the disadvantages described above.

SUMMARY OF THE INVENTION

The following description of various embodiments is not to be construedin any way as limiting the subject matter of the appended claims.

One embodiment relates to a system configured for shape metric basedsorting of wafer locations. The system includes one or more computersubsystems configured for selecting shape based grouping (SBG) rules forat least two locations on a wafer. For one of the locations on thewafer, selecting the SBG rule includes determining distances betweengeometric primitives in a field of view (FOV) centered on the onelocation by modifying distances between the geometric primitives in adesign for the wafer with metrology data for the one location on thewafer. Selecting the SBG rule for the one location also includesdetermining metrical complexity (MC) scores for SBG rules associatedwith the geometric primitives in the FOV based on the determineddistances between the geometric primitives. In addition, selecting theSBG rule for the one location includes selecting one of the SBG rulesfor the one location based on the MC scores. The one or more computersubsystems are also configured for sorting the at least two locations onthe wafer based on the SBG rules selected for the at least twolocations. The system may be further configured as described herein.

Another embodiment relates to a computer-implemented method for shapemetric based sorting of wafer locations. The method includes theselecting SBG rules and sorting steps described above. The steps of themethod are performed by one or more computer subsystems. Each of thesteps of the method described above may be further performed asdescribed further herein. In addition, the method described above mayinclude any other step(s) of any other method(s) described herein.Furthermore, the method described above may be performed by any of thesystems described herein.

An additional embodiment relates to a non-transitory computer-readablemedium storing program instructions executable on a computer system forperforming a computer-implemented method for shape metric based sortingof wafer locations. The computer-implemented method includes the stepsof the method described above. The computer-readable medium may befurther configured as described herein. The steps of thecomputer-implemented method may be performed as described furtherherein. In addition, the computer-implemented method for which theprogram instructions are executable may include any other step(s) of anyother method(s) described herein.

BRIEF DESCRIPTION OF THE DRAWINGS

Other objects and advantages of the invention will become apparent uponreading the following detailed description and upon reference to theaccompanying drawings in which:

FIGS. 1 and 2 are schematic diagrams illustrating a side view of anembodiment of a system configured as described herein;

FIG. 3 is a schematic diagram illustrating a side view of an embodimentof a metrology tool configured as described herein;

FIG. 4 is a schematic diagram illustrating a plan view of an embodimentof a system configured as described herein;

FIG. 5 is a schematic diagram illustrating a plan view of examples ofgeometric primitives and directional force fields emanating from andsurrounding them;

FIG. 6 is a schematic diagram illustrating a plan view of one embodimentof a nominal configuration of a shape based grouping rule;

FIGS. 7-9 are flow diagrams illustrating various embodiments of stepsthat may be performed by the embodiments described herein; and

FIG. 10 is a block diagram illustrating one embodiment of anon-transitory computer-readable medium storing program instructionsexecutable on a computer system for performing one or more of thecomputer-implemented methods described herein.

While the invention is susceptible to various modifications andalternative forms, specific embodiments thereof are shown by way ofexample in the drawings and will herein be described in detail. Itshould be understood, however, that the drawings and detaileddescription thereto are not intended to limit the invention to theparticular form disclosed, but on the contrary, the intention is tocover all modifications, equivalents and alternatives falling within thespirit and scope of the present invention as defined by the appendedclaims.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

The terms “design” and “design data” as used herein generally refer tothe physical design (layout) of an IC and data derived from the physicaldesign through complex simulation or simple geometric and Booleanoperations. The physical design may be stored in a data structure suchas a graphical data stream (GDS) file, any other standardmachine-readable file, any other suitable file known in the art, and adesign database. A GDSII file is one of a class of files used for therepresentation of design layout data. Other examples of such filesinclude GL1 and OASIS files and proprietary file formats such as RDFdata, which is proprietary to KLA-Tencor, Milpitas, Calif. In addition,an image of a reticle acquired by a reticle inspection system and/orderivatives thereof can be used as a “proxy” or “proxies” for thedesign. Such a reticle image or a derivative thereof can serve as asubstitute for the design layout in any embodiments described hereinthat use a design. The design may include any other design data ordesign data proxies described in commonly owned U.S. Pat. Nos. 7,570,796issued on Aug. 4, 2009 to Zafar et al. and 7,676,077 issued on Mar. 9,2010 to Kulkarni et al., both of which are incorporated by reference asif fully set forth herein. In addition, the design data can be standardcell library data, integrated layout data, design data for one or morelayers, derivatives of the design data, and full or partial chip designdata.

In some instances, simulated or acquired images from a wafer or reticlecan be used as a proxy for the design. Image analysis can also be usedas a proxy for design data. For example, polygons in the design may beextracted from an image of a design printed on a wafer and/or reticle,assuming that the image of the wafer and/or reticle is acquired withsufficient resolution to adequately image the polygons of the design. Inaddition, the “design” and “design data” described herein refers toinformation and data that is generated by semiconductor device designersin a design process and is therefore available for use in theembodiments described herein well in advance of printing of the designon any physical wafers,

The “design” or “physical design” may also be the design as it would beideally formed on the wafer. In this manner, a design described hereinmay not include features of the design that would not be printed on thewafer such as optical proximity correction (OPC) features, which areadded to the design to enhance printing of the features on the waferwithout actually being printed themselves.

Turning now to the drawings, it is noted that the figures are not drawnto scale. In particular, the scale of some of the elements of thefigures is greatly exaggerated to emphasize characteristics of theelements. It is also noted that the figures are not drawn to the samescale. Elements shown in more than one figure that may be similarlyconfigured have been indicated using the same reference numerals. Unlessotherwise noted herein, any of the elements described and shown mayinclude any suitable commercially available elements.

One embodiment relates to a system configured for shape metric basedsorting of wafer locations. Shape based grouping (SBG) rules encapsulateprior knowledge of hot spots at wafer locations due to design patterninfluence at those locations. These rules, expressed as spatialco-occurrence of and spatial relations between geometric primitives,have proven to be an important source of information in understandingsystematic defect formation. SBG has at least two major use cases asdescribed further herein: defining prioritized micro-care areas forinspection; and prioritizing samples for review.

The embodiments described herein enhance the complexity scores of SBGrules, by augmenting them with design metrics and metrologymeasurements, which provides a number of advantages including providingthe ability to achieve more finely tuned hot spot criticality scores.The study of defect sampling improvements using metrology measurementsand the remarkable efficacy of using SBG scores for process windowqualification (PWQ) sampling lead the inventors to the novelunderstanding that the union of these two, i.e., SBG combined withmetrology measurements and design metrics, will produce a superiorsampling method compared to existing ones. Furthermore, such a superiorsampling method can provide an “out-of-the-box” sampling method thatadvantageously requires little or no training.

One embodiment of such a system is shown in FIG. 1. In one embodiment,the system includes an output acquisition subsystem that includes atleast an energy source and a detector. The energy source is configuredto generate energy that is directed to a wafer. The detector isconfigured to detect energy from the wafer and to generate outputresponsive to the detected energy. The wafer may include any wafer knownin the art.

In one embodiment, the energy directed to the wafer includes light, andthe energy detected from the wafer includes light. For example, in theembodiment of the system shown in FIG. 1, output acquisition subsystem10 includes an illumination subsystem configured to direct light towafer 14. The illumination subsystem includes at least one light source.For example, as shown in FIG. 1, the illumination subsystem includeslight source 16. In one embodiment, the illumination subsystem isconfigured to direct the light to the wafer at one or more angles ofincidence, which may include one or more oblique angles and/or one ormore normal angles. For example, as shown in FIG. 1, light from lightsource 16 is directed through optical element 18 and then lens 20 tobeam splitter 21, which directs the light to wafer 14 at a normal angleof incidence. The angle of incidence may include any suitable angle ofincidence, which may vary depending on, for instance, characteristics ofthe wafer.

The illumination subsystem may be configured to direct the light to thewafer at different angles of incidence at different times. For example,the output acquisition subsystem may be configured to alter one or morecharacteristics of one or more elements of the illumination subsystemsuch that the light can be directed to the wafer at an angle ofincidence that is different than that shown in FIG. 1. In one suchexample, the output acquisition subsystem may be configured to movelight source 16, optical element 18, and lens 20 such that the light isdirected to the wafer at a different angle of incidence.

In some instances, the output acquisition subsystem may be configured todirect light to the wafer at more than one angle of incidence at thesame time. For example, the illumination subsystem may include more thanone illumination channel, one of the illumination channels may includelight source 16, optical element 18, and lens 20 as shown in FIG. 1 andanother of the illumination channels (not shown) may include similarelements, which may be configured differently or the same, or mayinclude at least a light source and possibly one or more othercomponents such as those described further herein. If such light isdirected to the wafer at the same time as the other light, one or morecharacteristics (e.g., wavelength, polarization, etc.) of the lightdirected to the wafer at different angles of incidence may be differentsuch that light resulting from illumination of the wafer at thedifferent angles of incidence can be discriminated from each other atthe detector(s).

In another instance, the illumination subsystem may include only onelight source (e.g., source 16 shown in FIG. 1) and light from the lightsource may be separated into different optical paths (e.g., based onwavelength, polarization, etc.) by one or more optical elements (notshown) of the illumination subsystem. Light in each of the differentoptical paths may then be directed to the wafer. Multiple illuminationchannels may be configured to direct light to the wafer at the same timeor at different times (e.g., when different illumination channels areused to sequentially illuminate the wafer). In another instance, thesame illumination channel may be configured to direct light to the waferwith different characteristics at different times. For example, in someinstances, optical element 18 may be configured as a spectral filter andthe properties of the spectral filter can be changed in a variety ofdifferent ways (e.g., by swapping out the spectral filter) such thatdifferent wavelengths of light can be directed to the wafer at differenttimes. The illumination subsystem may have any other suitableconfiguration known in the art for directing light having different orthe same characteristics to the wafer at different or the same angles ofincidence sequentially or simultaneously.

In one embodiment, light source 16 is a broadband plasma (BBP) lightsource. In this manner, the light generated by the light source anddirected to the wafer may include broadband light. However, the lightsource may include any other suitable light source such as a laser. Thelaser may include any suitable laser known in the art and may beconfigured to generate light at any suitable wavelength or wavelengthsknown in the art. In addition, the laser may be configured to generatelight that is monochromatic or nearly-monochromatic. In this manner, thelaser may be a narrowband laser. The light source may also include apolychromatic light source that generates light at multiple discretewavelengths or wavebands.

Light from optical element 18 may be focused to beam splitter 21 by lens20. Although lens 20 is shown in FIG. 1 as a single refractive opticalelement, it is to be understood that, in practice, lens 20 may include anumber of refractive and/or reflective optical elements that incombination focus the light from the optical element to the wafer. Theillumination subsystem shown in FIG. 1 and described herein may includeany other suitable optical elements (not shown). Examples of suchoptical elements include, but are not limited to, polarizingcomponent(s), spectral filter(s), spatial filter(s), reflective opticalelement(s), apodizer(s), beam splitter(s), aperture(s), and the like,which may include any such suitable optical elements known in the art.In addition, the system may be configured to alter one or more of theelements of the illumination subsystem based on the type of illuminationto be used for the wafer.

The output acquisition subsystem may also include a scanning subsystemconfigured to cause the light to be scanned over the wafer. For example,the output acquisition subsystem may include stage 22 on which wafer 14is disposed during output acquisition. The scanning subsystem mayinclude any suitable mechanical and/or robotic assembly (that includesstage 22) that can be configured to move the wafer such that the lightcan be scanned over the wafer. In addition, or alternatively, the outputacquisition subsystem may be configured such that one or more opticalelements of the output acquisition subsystem perform some scanning ofthe light over the wafer. The light may be scanned over the wafer in anysuitable fashion.

The output acquisition subsystem further includes one or more detectionchannels, At least one of the one or more detection channels includes adetector configured to detect light from the wafer due to illuminationof the wafer by the output acquisition subsystem and to generate outputresponsive to the detected light. For example, the output acquisitionsubsystem shown in FIG. 1 includes two detection channels, one formed bycollector 24, element 26, and detector 28 and another thrmed bycollector 30, element 32, and detector 34. As shown in FIG. 1, the twodetection channels are configured to collect and detect light atdifferent angles of collection. In some instances, one detection channelis configured to detect specularly reflected light, and the otherdetection channel is configured to detect light that is not specularlyreflected (e.g., scattered, diffracted, etc.) from the wafer. However,two or more of the detection channels may be configured to detect thesame type of light from the wafer (e.g., specularly reflected light).Although FIG. 1 shows an embodiment of the output acquisition subsystemthat includes two detection channels, the output acquisition subsystemmay include a different number of detection channels (e.g., only onedetection channel or two or more detection channels). Although each ofthe collectors are shown in FIG. 1 as single refractive opticalelements, it is to be understood that each of the collectors may includeone or more refractive optical element(s) and/or one or more reflectiveoptical element(s).

The one or more detection channels may include any suitable detectorsknown in the art. For example, the detectors may includephoto-multiplier tubes (PMTs), charge coupled devices (CCDs), and timedelay integration (TDI) cameras. The detectors may also includenon-imaging detectors or imaging detectors. If the detectors arenon-imaging detectors, each of the detectors may be configured to detectcertain characteristics of the scattered light such as intensity but maynot be configured to detect such characteristics as a function ofposition within the imaging plane. As such, the output that is generatedby each of the detectors included in each of the detection channels ofthe output acquisition subsystem may be signals or data, but not imagesignals or image data. In such instances, a computer subsystem such ascomputer subsystem 36 of the system may be configured to generate imagesof the wafer from the non-imaging output of the detectors. However, inother instances, the detectors may be configured as imaging detectorsthat are configured to generate imaging signals or image data.Therefore, the system may be configured to generate the output describedherein in a number of ways.

It is noted that FIG. 1 is provided herein to generally illustrate aconfiguration of an output acquisition subsystem that may be included inthe system embodiments described herein. Obviously, the outputacquisition subsystem configuration described herein may be altered tooptimize the performance of the system as is normally performed whendesigning a commercial system. In addition, the systems described hereinmay be implemented using an existing output acquisition system (e.g., byadding functionality described herein to an existing system) such as the29 xx, 39 xx, Archer, ATL, SpectraShape, SpectraFilm, Aleris, andWaferSight series of tools that are commercially available fromKLA-Tencor, Milpitas, Calif. For some such systems, the methodsdescribed herein may be provided as optional functionality of the system(e.g., in addition to other functionality of the system). Alternatively,the system described herein may be designed “from scratch” to provide acompletely new system.

Computer subsystem 36 of the system may be coupled to the detectors ofthe output acquisition subsystem in any suitable manner (e.g., via oneor more transmission media, which may include “wired” and/or “wireless”transmission media) such that the computer subsystem can receive theoutput generated by the detectors during scanning of the wafer. Computersubsystem 36 may be configured to perform a number of functions usingthe output of the detectors as described herein and any other functionsdescribed further herein. For example, in one embodiment, the one ormore computer subsystems included in the system are configured todetermine information for at least two locations on the wafer based onthe output.

The information determined by the computer subsystem(s) may varydepending on the configuration of the output acquisition subsystem. Forexample, if the output acquisition subsystem is configured as aninspection subsystem, then the information may include information fordefects detected on the wafer using the output. In one such example, thecomputer subsystem(s) are configured for detecting defects on the waferby applying a defect detection method to the output. Detecting defectson the wafer may be performed in any suitable manner known in the art(e.g., applying a defect detection threshold to the output anddetermining that any output having a value above the defect detectionthreshold corresponds to a defect or a potential defect) with anysuitable defect detection method and/or algorithm. In another example,if the output acquisition subsystem is configured as a metrologysubsystem, then the information may include information for one or morecharacteristics of the wafer or features formed on the wafer such as oneor more of film thickness, patterned structure profile, criticaldimension (CD), line edge roughness (LER), line width roughness (LWR),and overlay measurements. These one or more characteristics may bedetermined using the output as described further herein or in any othermanner known in the art. In an additional example, if the outputacquisition subsystem is configured as a defect review subsystem, thenthe information for the locations may be information for defects at thelocations generated by defect review. That information may include oneor more characteristics of the defects such as size, shape, texture,etc. and/or defect classification information such as a defect type ID.The defect information may be determined as described further herein orin any other manner known in the art. This computer subsystem may befurther configured as described herein.

This computer subsystem (as well as other computer subsystems describedherein) may also be referred to herein as computer system(s). Each ofthe computer subsystem(s) or system(s) described herein may take variousforms, including a personal computer system, image computer, mainframecomputer system, workstation, network appliance, Internet appliance, orother device. In general, the term “computer system” may be broadlydefined to encompass any device having one or more processors, whichexecutes instructions from a memory medium. The computer subsystem(s) orsystem(s) may also include any suitable processor known in the art suchas a parallel processor. In addition, the computer subsystem(s) orsystem(s) may include a computer platform with high speed processing andsoftware, either as a standalone or a networked tool.

If the system includes more than one computer subsystem, then thedifferent computer subsystems may be coupled to each other such thatimages, data, information, instructions, etc. can be sent between thecomputer subsystems as described further herein. For example, computersubsystem 36 may be coupled to computer subsystem(s) 102 (as shown bythe dashed line in FIG. 1) by any suitable transmission media, which mayinclude any suitable wired and/or wireless transmission media known inthe art. Two or more of such computer subsystems may also be effectivelycoupled by a shared computer-readable storage medium (not shown).

In another embodiment, the energy directed to the wafer includeselectrons and the energy detected from the wafer includes electrons. Inthis manner, the output acquisition subsystem is configured as anelectron beam output acquisition subsystem. In one such embodiment shownin FIG. 2, the output acquisition subsystem includes electron column122, which is coupled to computer subsystem 124. As also shown in FIG.2, the electron column includes electron beam source 126 configured togenerate electrons that are focused to wafer 128 by one or more elements130. The electron beam source may include, for example, a cathode sourceor emitter tip, and one or more elements 130 may include, for example, agun lens, an anode, a beam limiting aperture, a gate valve, a beamcurrent selection aperture, an objective lens, and a scanning subsystem,all of which may include any such suitable elements known in the art.

Electrons returned from the wafer (e.g., secondary electrons) may befocused by one or more elements 132 to detector 134. One or moreelements 132 may include, for example, a scanning subsystem, which maybe the same scanning subsystem included in element(s) 130.

The electron column may include any other suitable elements known in theart. In addition, the electron column may be further configured asdescribed in U.S. Pat. Nos. 8,664,594 issued Apr. 4, 2014 to Jiang etal., 8,692,204 issued Apr. 8, 2014 to Kojima et al., 8,698,093 issuedApr. 15, 2014 to Gubbens et al., and 8,716,662 issued May 6, 2014 toMacDonald et al., which are incorporated by reference as if fully setforth herein.

Although the electron column is shown in FIG. 2 as being configured suchthat the electrons are directed to the wafer at an oblique angle ofincidence and are scattered from the wafer at another oblique angle, itis to be understood that the electron beam may be directed to andscattered from the wafer at any suitable angles. In addition, theelectron beam subsystem may be configured to use multiple modes togenerate images of the wafer (e.g., with different illumination angles,collection angles, etc.). The multiple modes of the electron beamsubsystem may be different in any image generation parameters of thesubsystem.

Computer subsystem 124 may be coupled to detector 134 as describedabove. The detector may detect electrons returned from the surface ofthe wafer thereby forming electron beam images of the wafer. Theelectron beam images may include any suitable electron beam images.Computer subsystem 124 may be configured to perform any of the functionsdescribed herein using the output of the detector and/or the electronbeam images. Computer subsystem 124 may be configured to perform anyadditional step(s) described herein. A system that includes the electronbeam subsystem shown in FIG. 2 may be further configured as describedherein.

It is noted that FIG. 2 is provided herein to generally illustrate aconfiguration of an output acquisition subsystem that may be included inthe embodiments described herein. As with the optical subsystemdescribed above, the electron beam subsystem described herein may bealtered to optimize the performance of the electron beam subsystem as isnormally performed when designing a commercial system. In addition, thesystems described herein may be implemented using an existing electronbeam system (e.g., by adding functionality described herein to anexisting electron beam system). For some such systems, the methodsdescribed herein may be provided as optional functionality of the system(e.g., in addition to other functionality of the system). Alternatively,the system described herein may be designed “from scratch” to provide acompletely new system.

Although the output acquisition subsystem is described above as being anoptical or electron beam subsystem, the output acquisition subsystem maybe an ion beam subsystem. Such an output acquisition subsystem may beconfigured as shown in FIG. 2 except that the electron beam source maybe replaced with any suitable ion beam source known in the art. Inaddition, the output acquisition subsystem may be any other suitable ionbeam tool such as those included in commercially available focused ionbeam (FIB) systems, helium ion microscopy (HIM) systems, and secondaryion mass spectroscopy (SIMS) systems.

As noted above, the output acquisition subsystem may be configured fordirecting energy (e.g., light, electrons) to and/or scanning energy overa physical version of the wafer thereby generating actual (i.e., notsimulated) output and/or images for the physical version of the wafer.In this manner, the output acquisition subsystem may be configured as an“actual” tool, rather than a “virtual” tool. Computer subsystem(s) 102shown in FIG. 1 may, however, include one or more “virtual” systems (notshown) that are configured for performing one or more functions using atleast some of the actual output or images generated for the water, whichmay include any of the one or more functions described further herein.

The one or more virtual systems are not capable of having the waferdisposed therein. In particular, the virtual system(s) are not park ofoutput acquisition subsystem 10 or electron column 122 and do not haveany capability for handling the physical version of the wafer. In otherwords, in a virtual system, the output of its one or more “detectors”may be output that was previously generated by one or more detectors ofan actual subsystem and that is stored in the virtual system, and duringthe “imaging and/or scanning,” the virtual system may replay the storedoutput as though the wafer is being imaged and/or scanned. In thismanner, imaging and/or scanning the wafer with a virtual system mayappear to be the same as though a physical water is being imaged and/orscanned with an actual system, while, in reality, the “imaging and/orscanning” involves simply replaying output for the wafer in the samemanner as the wafer may be imaged and/or scanned.

Systems and methods configured as “virtual” inspection systems aredescribed in commonly assigned U.S. Pat. Nos. 8,126,255 issued on Feb.28, 2012 to Bhaskar et al. and 9,222,895 issued on Dec. 29, 2015 toDuffy et al., both of which are incorporated by reference as if fullyset forth herein. The embodiments described herein may be furtherconfigured as described in these patents. For example, the one or morecomputer subsystems described herein may be further configured asdescribed in these patents.

The output acquisition subsystems described herein may be configured togenerate output, e.g., images, of the wafer with multiple modes. Ingeneral, a “mode” is defined by the values of parameters of the outputacquisition subsystem used for generating images of a wafer or theoutput used to generate images of the wafer. Therefore, modes that aredifferent may be different in the values for at least one of theparameters of the output acquisition subsystem. In this manner, in someembodiments, the output includes images generated by the outputacquisition subsystem with two or more different values of a parameterof the output acquisition subsystem. For example, in one embodiment ofan optical subsystem, different modes may use different wavelengths oflight for illumination. The modes may be different in the illuminationwavelengths as described further herein (e.g., by using different lightsources, different spectral filters, etc.) for different modes. Inanother embodiment, different modes may use different illuminationchannels. For example, as noted above, the output acquisition subsystemmay include more than one illumination channel. As such, differentillumination channels may be used for different modes.

In a similar manner, the output generated by the electron beam subsystemmay include output, e.g., images, generated by the electron beamsubsystem with two or more different values of a parameter of theelectron beam subsystem. The multiple modes of the electron beamsubsystem can be defined by the values of parameters of the electronbeam subsystem used for generating output and/or images for a wafer.Therefore, modes that are different may be different in the values forat least one of the electron beam parameters of the electron beamsubsystem. For example, in an electron beam subsystem, different modesmay use different angles of incidence for illumination.

The one or more computer subsystems are configured for selecting SBGrules for at least two locations on a wafer. In other words, for eachlocation on the wafer for which the steps described herein areperformed, the computer subsystem(s) may select one SBG rule (i.e., oneSBG rule per location under consideration). Although some of the stepsare described herein as being performed for one location for the sake ofsimplicity, each of the steps described herein may be performedseparately and independently for each of the locations for which SBGrules are being selected.

In one embodiment, the at least two locations on the wafer includelocations of defects detected on the wafer by inspection. The locationsof the defects detected on the wafer may be determined by theembodiments described herein (e.g., by the computer subsystem(s) coupledto the output acquisition subsystems described herein) or by anothersystem that performs inspection of the wafer. The locations may bedetermined in any suitable manner. The steps described herein may beperformed for defect locations if, for example, the steps are performedto generate a sampling scheme for the defects and/or to generate asample of the defects. In another embodiment, the at least two locationsinclude SBG rule hit locations. The SBG rule hit locations may bedetermined as described herein by the embodiments described herein or byanother system or method. The steps described herein may be performedfor SBG rule hit locations if, for example, the steps are performed togenerate care areas for the wafer.

For one of the locations on the wafer, selecting the SBG rule includesdetermining distances between geometric primitives in a field of view(FOV) centered on the one location by modifying distances between thegeometric primitives in a design for the wafer with metrology data forthe one location on the wafer. The term “geometric primitive” as usedherein (and commonly used in the art) is defined as at least a portionof a patterned feature that is or will be formed on a specimen such as awafer. In one such example, a patterned feature that is or will beformed on a wafer may be defined by or broken up into the geometricprimitives that define it as a feature. Some examples of geometricprimitives are described further herein for illustrative purposes, whilealso noting that the embodiments described herein are not limited tothese or any geometric primitives.

The FOV may have predetermined dimensions such as those describedfurther herein. The FOV may or may not be different than a FOV of ametrology, inspection, defect review, or other tool that performs animaging or measurement process on a wafer. For example, the FOV may havedimensions in the design or on the wafer that correspond to thedimensions of a FOV of an inspection tool on the wafer. However, the FOVmay have dimensions in the design or on the wafer that are determinedbased on knowledge of the design itself, e.g., its design rule ordimensions of one or more patterned features in the design (so that theFOV is large enough to capture a suitable number of patterned featuresat or near the location), information for how the measurements or imagesgenerated for the location will be processed (e.g., information for howmany pixels are in a “job” performed for the location in which all ofthe pixels are collectively processed for inspection, defect review,metrology, etc.), and the like.

As described further herein, geometric primitives may have differentcharacteristics as formed on a wafer compared to as designed in thedesign data for the wafer. The characteristics of the geometricprimitives as they are formed on the wafer cannot be determined simplyfrom design data. Instead, the embodiments described herein usemetrology data for modifying such characteristics including thedistances between geometric primitives in the FOV. The distances betweenthe geometric primitives are determined in the embodiments describedherein because they can have an effect on the metrical complexity (MC)of the SBG rules determined as described herein. Therefore, to determinethe MC scores of the SBG rules with relatively high accuracy, it isimportant to take into account any variation in distance between thegeometric primitives on the wafer compared to in the design for thewafer.

As described further herein, the metrology data for the one location onthe wafer that is used to determine the distances between the geometricprimitives in the FOV centered on the one location may not be metrologydata generated at that location by a metrology tool. For example, unlikethe case in which local design dimensions can be measured directly andeasily through design data, the local design dimensions on a wafer maybe estimated (or predicted, e.g., through interpolation) based onmetrology data generated at certain predetermined metrology targetpoints on the wafer (having specific patterns) that may or may not besubstantially close to the actual location. However, using theembodiments described herein, in the case of a CD estimation process,the computer subsystem(s) could determine the changes (e.g., dilation orerosion) of each polygon at any wafer location (measured or not) andapply these geometric transformations to the design at that location.Similarly, after an overlay estimation process, the shifts of eachpattern (or any patterns) printed on the wafer with a different mask inthe +/−x and +/−y directions can be estimated (i.e., predicted, e.g.,via interpolation) at every wafer location (measured or not), and thecomputer subsystem(s) can overlay correct the design at each of thoselocations with these shift estimates.

In one embodiment, the computer subsystem(s) are configured foracquiring the metrology data for the wafer from a metrology tool thatperforms measurements on the wafer at an array of measurement points onthe wafer and assigning the metrology data to the at least two locationson the wafer based on positions of the at least two locations on thewafer determined with respect to locations of the measurement points onthe wafer. Metrology and inspection are generally treated as separatedomains in semiconductor manufacturing. For example, metrology istypically calibrated to a reference standard, and inspection istypically performed by comparing acquired signal results (output,signals, images, etc.) from proximate structures (e.g., within die,die-to-die, etc.) or versus a stored reference (recorded or generatedthrough simulation or otherwise synthesized).

The metrology tool may have any suitable configuration known in the art.In one example, the output acquisition subsystems shown in FIGS. 1 and 2may be configured and used as metrology subsystems. In particular, theembodiments of the output acquisition subsystems described herein andshown in FIGS. 1 and 2 may be modified in one or more parameters toprovide different capability depending on the application for which theywill be used. In one such example, the output acquisition subsystemsshown in FIGS. 1 and 2 may be configured to have a higher resolution ifthey are to be used for metrology rather than for inspection. In otherwords, the embodiments of the output acquisition subsystems shown inFIGS. 1 and 2 illustrate some general and various configurations thatcan be tailored in a number of manners that will be obvious to oneskilled in the art to produce subsystems having different capabilitiesthat are more or less suitable for different applications such asinspection and/or metrology. In addition, if the same subsystem hasvariable hardware settings such that it can be used for multipleapplications (e.g., both inspection and metrology), then the samesubsystem can be used for both inspection and metrology. In a similarmanner, the output acquisition subsystems shown in FIGS. 1 and 2 may beconfigured as defect review subsystems.

An output acquisition subsystem configured for inspection will, however,generally be configured to have a resolution lower than the resolutionof a metrology tool during a metrology process (or a defect review toolduring a defect review process). For example, even if the outputacquisition subsystems described herein are configurable to haverelatively high resolutions that would render them suitable formetrology (or defect review), during an inspection process, the outputacquisition subsystem would be configured for a lower resolution toimprove the throughput of the inspection process (especially since sucha high resolution is not typically necessary or required for theinspection processes described herein).

FIG. 3, however, shows another embodiment of a metrology tool that mayperform measurements on the wafer as described herein. In the case of anoptical metrology tool, the metrology tool may include an illuminationsubsystem configured to direct light having one or more illuminationwavelengths to a wafer. For example, in the metrology tool embodimentshown in FIG. 3, the illumination subsystem of metrology tool 300includes light source 302, which may include any of the light sourcesdescribed herein. Light generated by light source 302 may be directedthrough one or more spectral filters 304 of the illumination subsystem.Spectral filter(s) 304 may be configured as described further herein.The illumination subsystem may also include beamsplitter 306 that isconfigured to reflect light from the spectral filter(s) to objective 308of the illumination subsystem. Beamsplitter 306 and objective 308 may befurther configured as described herein. Objective 308 is configured tofocus light having the one or more illumination wavelengths from thebeamslitter to wafer 310, which may include any of the wafers describedherein.

In one embodiment, the illumination subsystem includes a broadband lightsource. For example, light source 302 shown in FIG. 3 may be a broadbandlight source, and one or more spectral filters 304 may be positioned ina path of light from the broadband light source. Therefore, themetrology tool may include a broadband source with a selectablewavelength range for illumination through wavelength dependent filters.For example, the wavelength(s) directed to the wafer may be altered bychanging or removing the spectral filter(s) positioned in the path ofthe light from the light source. In this manner, the metrology tool maybe configured to have flexible illumination wavelength(s) that can bevaried depending on the materials on the wafer.

The metrology tool may also incorporate narrower or modified bandpassfilters into the illumination subsystem. In one such embodiment, the oneor more spectral filters include one or more interference filters. Forexample, spectral filter(s) 304 may be interference filter(s). In thismanner, the metrology tool may include a broadband source with aselectable wavelength range for illumination through interferencefilters. These filters can complement or replace bandpass filterscurrently being used in tools.

In additional embodiments, the illumination subsystem includes one ormore narrowband light sources or one or more laser light sources. Thenarrowband and/or laser light sources may include any suitable suchlight sources such as one or more diode lasers, diode-pumped solid state(DPSS) lasers, gas lasers, etc. In addition, the illumination subsystemsdescribed herein may include any number of broadband, narrowband, andlaser light sources in any suitable combination. Furthermore, the lightsources may be quasi-monochromatic light sources. Any of the lightsources and illumination subsystem configurations described herein maybe included in a metrology tool having any suitable configuration.Therefore, many different combinations of light sources and metrologytool configurations are possible and may be selected depending on, forexample, the wafer and/or wafer characteristics that are to be measuredby the tool.

The illumination subsystem may be configured in a number of differentways for selective illumination angle and/or polarization. For example,the illumination angle may be altered or selected by changing a positionof a light source of the illumination subsystem or by controlling one ormore other elements of the illumination subsystem that affect theillumination angle. The illumination angle that is altered or selectedmay be the polar angle and/or the azimuthal angle of the incident light.In addition, the illumination polarization may be selected by selectinga light source that emits light having the selected polarization or byincluding one or more polarization selection/alteration/filteringelements in the path of the light emitted by the tight source.

The metrology tool also includes a detection subsystem configured todetect light from the wafer. As shown in FIG. 3, the detection subsystemincludes objective 308 configured to collect light from wafer 310. Thecollected light may include specularly reflected light and/or scatteredlight. The detection subsystem may also include beamsplitter 306configured to transmit the light collected by the objective lens.

In some cases, the detection subsystem includes beamsplitter 312positioned in the path of the light transmitted by beamsplitter 306 andconfigured to transmit light having one or more wavelengths and reflectlight having one or more other wavelengths. The detection subsystem mayalso include one or more bandpass filters 314 that may be configured asdescribed further herein and may transmit light having one or moreselected wavelengths. One or more of beamsplitter 306, beamsplitter 312,and bandpass filter(s) 314 may be configured to selectively transmitlight having one or more selected wavelengths and to reflect orotherwise block light that does not have the one or more selectedwavelengths out of the detection path of the detection subsystem suchthat they are not detected by detector 316.

The detection subsystem may also include one or more bandpass filters318 and detector 320. In the configuration shown in FIG. 3, lightreflected by beamsplitter 312 is directed to one or more bandpasstitters 318, and light transmitted by the one or more bandpass filtersis detected by detector 320. Bandpass filter(s) 318 and detector 320 maybe further configured as described herein. Beamsplitter 312 may beconfigured to transmit light having one or more first wavelengths and toreflect light having one or more second wavelengths different than thefirst wavelength(s). In this manner, detectors 316 and 320 may detectlight having different wavelengths.

In one embodiment, the illumination and detection subsystems include acommon objective lens and a common dichroic mirror or beamsplitter,which are configured to direct the light from a light source of theillumination subsystem to the wafer and to direct the light from thewafer to a detector of the detection subsystem. For example, as shown inFIG. 3, the illumination and detection subsystems may both includeobjective 308 making it a common objective lens and beamsplitter 306making it a common dichroic mirror or beamsplitter. As described above,objective 308 and beamsplitter 306 are configured to direct the lightfrom light source 302 of the illumination subsystem to wafer 310 and todirect the light from the wafer to detector 316 and/or detector 320 ofthe detection subsystem.

In one embodiment, one or more wavelengths of the light detected by thedetection subsystem are selected by altering one or more parameters ofthe detection subsystem based on one or more materials on the wafer, oneor more characteristics of the wafer that are being measured, or somecombination thereof. Therefore, like the illumination wavelength range,the detection wavelength range can be adjusted depending on the wafermaterials and the wafer characteristic(s) being measured. Thewavelength(s) detected by the detection subsystem may be altered asdescribed herein (e.g., using bandpass filter(s)) or in any othersuitable manner known in the art.

In one embodiment, the detection subsystem includes two or more channelsconfigured to separately and simultaneously detect the light from thewafer in different wavelength ranges. For example, the metrology toolcan be configured to include multiple parallel imaging channels thatimage varying wavelength ranges through suitable selection of dichroicand bandpass filter components. In the embodiment shown in FIG. 3, oneof the channels may include bandpass filter(s) 314 and detector 316 andthe other of the channels may include bandpass filter(s) 318 anddetector 320. In addition, the metrology tool may include more than twochannels (e.g., by insertion of one or more additional beamsplitters(not shown) into the path of the light from the wafer, each of which maybe coupled to a detector (not shown) and possibly spectral filters (notshown) and/or other optical elements (not shown)). The channel includingbandpass filters(s) 314 and detector 316 may be configured to detectlight in a first wavelength band, and the channel that includes bandpassfilter(s) 318 and detector 320 may be configured to detect light in asecond wavelength band. In this manner, different wavelength ranges oflight may be detected by different channels simultaneously. In addition,the different wavelength ranges may be mutually exclusive (e.g.,separated by one or more wavelengths) or may overlap entirely (e.g., onewavelength range may be entirely within another wavelength range) orpartially (e.g., multiple wavelength ranges may include the same one ormore wavelengths, but at least some of the wavelengths in a firstwavelength range are mutually exclusive of at least some of thewavelengths in a second wavelength range, and vice versa). In someembodiments, the detection subsystem includes a spectrometer configuredto measure a characteristic of the light from the wafer across awavelength range. For example, in the embodiment shown in FIG. 3, one ormore of detectors 316 and 320 may be a spectrometer.

As described above, the detection subsystem may be configured toselectively and separately detect the light from the wafer based on thewavelength of the light. In a similar manner, if the illuminationsubsystem is configured for selective illumination angle and/orpolarization, the detection subsystem may be configured for selectivedetection of light based on angle from the wafer (or collection angle)and/or polarization. For example, the detection subsystem may includeone or more apertures ((not shown) that can be used to control thecollection angles of the light detected by the detection subsystem. Inanother example, the detection subsystem may include one or morepolarizing components (not shown) in the path of the light from thewafer that can be used to control the polarizations of the lightdetected by the detection subsystem.

The metrology tool also includes a computer subsystem configured togenerate metrology data for the wafer using output generated by thedetection subsystem responsive to the detected light. For example, inthe embodiment shown in FIG. 3, the metrology tool may include computersubsystem 322, which may be coupled to detectors 316 and 320 by one ormore transmission media shown in FIG. 3 by the dashed lines, which mayinclude “wired” and/or “wireless” transmission media, such that thecomputer subsystem can receive output generated by the detectors of thedetection subsystem that is responsive to the detected light. One outputof the detectors may include, for example, signals, images, data, imagedata, and the like. For example, the detector(s) may be imagingdetectors that are configured to capture image(s) of the wafer. Thecomputer subsystem may be further configured as described herein. Themetrology data may be any of the metrology data described herein. Themetrology data may be stored in (or Output as) a metrology results file.

It is noted that FIG. 3 is provided herein to generally illustrate someconfigurations of the metrology tool embodiments described herein.Obviously, the metrology tool configurations described herein may bealtered to optimize the performance of the metrology tool as is normallyperformed when designing a commercial metrology tool.

In addition, the metrology tools described herein may include anexisting metrology tool (e,g., by adding functionality described hereinto an existing metrology tool) such as Archer, ATL, SpectraShape,SpectraFilm, Aleris, WaferSight, Therma-Probe, RS-200, CIRCL, andProfiler tools that are commercially available from KLA-Tencor. For somesuch systems, the methods described herein may be provided as optionalfunctionality of the existing metrology tool (e.g., in addition to otherfunctionality of the existing tool). Alternatively, the metrology tooldescribed herein may be designed “from scratch” to provide a completelynew system.

Although the metrology tool shown in FIG. 3 is a light-based or opticaltool, it is to be understood that the metrology tool may be configuredto also or alternatively use a different type of energy to perform themeasurements described herein. For example, the metrology tool may be anelectron beam-based tool such as a scanning electron microscope (SEM) ora transmission electron microscope (TEM) and/or a charged particlebeam-based tool such as a focused ion beam (FIB) tool. Such metrologytools may include any suitable commercially available metrology tool.

As described above, the metrology data is generated for the wafer by ametrology tool that performs measurements on the wafer at an array ofmeasurement points on the wafer. The array of measurement points may bea regular array of measurement points, but that is not necessary for theembodiments described herein. In addition, the array of measurementpoints may be a two-dimensional array of measurement points on thewafer.

A density of the measurement points on the wafer, which may bedetermined as described further herein, may be used to determine theexact locations of the measurement points on the wafer. For example,based on the selected or predetermined density of the measurement pointson the wafer, the measurement points can be evenly or regularly spacedacross the wafer such that the measurement points have the selected ordesired density across the wafer.

In one embodiment, the metrology tool generates the metrology data forthe wafer prior to inspection of the wafer. In another embodiment, themeasurement points are determined prior to inspection of the wafer andindependently of defects detected on the wafer. Generating the metrologydata (and optionally acquiring it) before inspection of the wafer can beadvantageous for a number of reasons. For instance, as described furtherherein, if the metrology data is generated prior to defect detection,the metrology data can be used before inspection of the wafer, e.g., togenerate care areas that are used during inspection of the wafer. Evenif the metrology data is not used before or during the inspection (andis instead used after the defects have been detected on the wafer and/orscanning of the wafer is completed), the metrology data may still begenerated prior to any scanning, defect detection, or inspection of thewafer layer on which the measurements were performed. In addition, ifthe metrology data is generated prior to inspection of the wafer asdescribed herein, then the measurement points must be determinedindependently of the detected defects because the defects have not beendetected on the wafer prior to the measurements and are therefore notavailable for use in determining Where the measurement points are to belocated.

As described further herein, the exact locations of the measurementpoints may be different than the exact locations of the defects detectedon the wafer and may be determined based on the desired density of themeasurement points on the wafer, which will in general be different thanthe density of the defects on the wafer. For example, the measurementpoints may be arranged in an array (e.g., a two-dimensional array) onthe wafer, and the density of the points in the array may be determinedas described further herein. In contrast, defects may be detected on thewafer at a much higher density and frequency than the measurement pointson the wafer. Furthermore, the measurement point locations may bedetermined independently of the defects detected on the wafer becausethe purpose of the measurements is not necessarily to measure one ormore characteristics of defects on the wafer but to measure variationsin one or more characteristics of the wafer, which may include one ormore materials on the wafer and/or one or more patterned structures onthe wafer.

In some contexts, the one or more characteristics of the wafer may beconsidered defects. For example, one linkage between the metrology andinspection domains is that defects may occur during wafer processing ifthe characteristic(s) reach sufficiently large deviations fromspecifications. In the embodiments described herein, thecharacteristic(s) of the wafer are purposefully being measuredregardless of whether the variations render the characteristic(s)defective, If a defect happens to be present at one of the preselectedmeasurement points, it may actually affect the metrology data generatedat that measurement point. However, such measurements (of defects orcharacteristic(s) that render the wafer defective) are not the goal ofthe measurements described herein.

In one embodiment, at least some values of the metrology data generatedby the metrology tool are below a resolution limit of an inspection toolthat performs inspection of the wafer. For instance, the metrology toolmay be configured to have a higher resolution than the inspection tool,including optical inspection tools as well as electron beam inspectiontools. Therefore, the inspection tool will have a resolution that islower than the metrology tools described herein that will be used toperform the measurements described herein. In this manner, theinspection tools are configured such that the output generated by theseinspection subsystems cannot be used to determine such variations.

In some embodiments, a density of the measurement points on the wafer isless than a density of inspection points on the wafer at which output isgenerated by a detector of an inspection tool during inspection of thewafer. Metrology as that term is used herein is performed independentlyof inspection and typically with lower frequency. For example, ingeneral, the measurement points will be spaced much farther apart fromeach other than the inspection points are spaced from each other. Inparticular, in most inspections that are performed on wafers, inspectionpoints generally overlap with one another (as the light, electrons, etc.are scanned across the wafer) so that no portion of the area to beinspected on the wafer does not undergo inspection. Such overlap of theinspection points is therefore by design. Therefore, the density of theinspection points is so high that the inspection points overlap witheach other at least a little bit. In contrast, it is desirable to selector determine the density of the measurement points to be as low aspossible (for throughput and cost considerations) while still beingsufficiently responsive to the variations of interest (described furtherherein) in the measurements. For example, the desired measurements(e.g., wafer topology, film thickness, CD, etc.) are performed atmeasurement points on the wafer that are dense enough to allow reliableprediction (e.g., interpolation, extrapolation, etc.) of themeasurements to any point on the wafer.

As used herein, the term “point” as in “measurement point” or“inspection point” does not necessarily mean that the measurement orinspection is a “point” measurement or inspection. In other words, asused herein, the term “point” is meant to indicate a location or area atwhich a measurement is performed or inspection output is acquired. Themeasurement or inspection that is performed at any one “point” mayhowever be performed across a relatively small area on the wafer (e.g.,a spot or area on the wafer). In this manner, a “measurement point” asused herein is meant to indicate a location or area on a wafer at whicha measurement is performed by a metrology tool, and each of the“measurement points” are discrete from one another on the wafer. Inaddition, an “inspection point” as used herein is meant to indicate anarea on a wafer at which inspection output is generated by an inspectiontool, but not each of the “inspection points” are necessarily discreteor spaced from each other because they will in general overlap with oneanother as inspection is normally performed.

Different minimum “densities” of measurement points may be used fordifferent use cases. For example, film thickness tends to varyrelatively slowly across wafers so the density of film thicknessmeasurements could be relatively low. On PWQ wafers, certain CDmeasurements can be performed per modulation to get more reliablemeasurements. Therefore, a density of the measurement points that is“dense enough” for the embodiments described herein includes any densityof measurement points that is large enough to make the prediction of themetrology data to the non-measured wafer locations sufficientlyaccurate.

The metrology data can be determined or generated from the measurementsperformed at the measurement points in any suitable manner. In otherwords, many different methods, algorithms, models, functions, etc. areavailable in the art to determine the metrology data from themeasurements. The metrology data used in the embodiments describedherein may be generated in any of these known ways. In addition,metrology analysis (e.g., modeling of overlay and other metrology data)may be performed on the 5D Analyzer system, which is commerciallyavailable from KLA-Tencor. This system is established in the industryand contains capabilities for advanced metrology analysis. Metrologydata can be delivered from this system or directly from the metrologytool if no further modeling is required.

In one embodiment, the metrology data includes one or more of filmthickness, patterned structure profile, CD, line edge roughness (LER),line width roughness (LWR), and overlay measurements. For example, thewafer characteristic(s) that may be particularly useful in embodimentsdescribed herein include targeted CD measurements such as line width,line roughness (CD uniformity) in particular structures, overlaymeasurements, and any other such characteristic(s) that can affect thedistances between geometric primitives on the wafer. In other words, themetrology data described herein can include any and all measurementsand/or wafer characteristic(s) that have an effect on the distancesbetween geometric primitives. LER and LWR and methods for measuring anddetermining these characteristics are described in Chapter 2 of“Variation-Aware Advanced CMOS Devices and SRAM” by Shin, SpringerNetherlands, 2016, pp. 19-35, which is incorporated by reference as iffully set forth herein. The measurements described herein may also beperformed as described in commonly assigned U.S. Patent ApplicationPublication No. 2016/0116420 by Duffy et al. published Apr. 28, 2018,which is incorporated by reference as if fully set forth herein. Theembodiments described herein may be further configured as described inthese references.

In another embodiment, the metrology data includes one or more oflithography focus metrology and scanner leveling data. In an additionalembodiment, the metrology data includes measurements of a characteristicof the wafer known to correlate with patterning defects. For example,focus error on a scanner (i.e., the tool used to print a pattern on awafer) may be of concern because such focus error can lead to patterningdefects on the wafer. Therefore, the metrology data may include anymeasurements of the wafer that relate to the lithography focus known inthe art. In addition, the scanner leveling data may be acquired from thescanner itself or from measurements performed while the wafer ispositioned in the scanner or while the scanner is otherwise printing thepattern on the wafer. The scanner leveling data may be generated and/oracquired in any suitable manner known in the art. Furthermore, themeasurements of the characteristic of the wafer known to correlate withpatterning defects may include some of the metrology data describedabove such as film thickness, which can cause patterning defects. Otherexamples of such measurements may include, but are not limited to,flatness of the wafer (which may be characterized by variations in filmthickness across the wafer and/or variation in the position of theuppermost surface of the wafer relative to the scanner caused by, forexample, warping of the wafer), variation in refractive index (orindices) of one or more materials on the wafer during the printing ofthe pattern on the wafer, relative or absolute locations of patternedfeatures underlying the layer in which the patterned features are beingprinted, and the like. These measurements may be performed in anysuitable manner known in the art.

In one embodiment, the metrology tool is not included in the system. Forexample, the metrology tool may be included in one system that isdifferent and separate from the system embodiments described herein. Inother words, the metrology tool may be included in a system that isphysically separate from the embodiments described herein and may notshare any common elements with the system embodiments described herein.In particular, as shown in FIGS. 1 and 3, the inspection subsystem maybe included in one system, the metrology tool may be configured asanother system, and the system and metrology tool are completelyphysically separate from each other and share no common hardwareelements.

In such embodiments, the one or more computer subsystems describedherein may be configured to access and acquire the metrology data from acomputer subsystem coupled to the metrology tool and/or a storage mediumin which the metrology data has been stored by the metrology tool. Theone or more computer subsystems may acquire the metrology data fromanother computer system or subsystem or a storage medium as describedfurther herein. In this manner, the metrology toot and the system thatincludes the one or more computer subsystems described herein may bedifferent tools. The metrology data can be stored in a database (such asKlarity, commercially available from KLA-Tencor), from where themeasurements can be retrieved by the embodiments described herein.

In this manner, acquiring the metrology data does not necessarilyinclude generating the metrology data. For example, as described above,the metrology tool may be configured to generate the metrology data andthen a computer subsystem described herein may acquire the metrologydata from the metrology toot, a computer subsystem of the metrologytool, or a storage medium in which the metrology data has been stored.As such, the metrology data that is acquired may have been generated bya system other than the embodiments described herein. However, in someembodiments, acquiring the metrology data includes generating themetrology data. For example, the embodiments described herein mayinclude a metrology tool (as described further herein), and thereforethe system embodiments described herein may be configured for generatingthe metrology data by performing the measurements on the wafer at themeasurement points. Alternatively, the system embodiments (or one ormore elements of the system) described herein may be configured to causethe metrology toot to perform the measurements on the wafer. Therefore,acquiring the metrology data may include performing the measurements onthe wafer at the measurement points.

In one embodiment, the metrology tool is incorporated into the systemsuch that the inspection tool and the metrology tool share one or morecommon elements of the system. FIG. 4 illustrates one embodiment of sucha system. The system includes inspection tool module 400 and metrologytool module 402. The inspection tool included in module 400 may beconfigured as described herein with respect to FIGS. 1 and 2. Themetrology tool included in module 402 may be configured as describedherein with respect to FIG. 3. The system may also include computersubsystem 404 coupled to one or both of the inspection tool and themetrology tool. Computer subsystem 404 may be configured according toany other embodiments described herein.

In some embodiments, the system also includes additional module 412, andthe is additional module may be configured to perform one or moreadditional processes on the wafer. The one or more additional processesmay include, for example, defect review, defect repair, and/or any otherquality-control related processes.

The one or more common elements that may be shared by the metrology andthe inspection tools may include one or more of common housing 406,common wafer handler 408, common power source 410, computer subsystem404, or some combination thereof. The common housing may have anysuitable configuration known in the art. For example, an originalhousing of the system may simply be expanded to accommodate both themetrology and inspection tools. In this manner, the metrology andinspection tools may be configured as a single unit or tool. The commonwafer handler may include any suitable mechanical and/or roboticassembly known in the art. The common wafer handler may be configured tomove the wafer between the metrology and inspection tools in such a waythat a wafer can be moved from the metrology tool directly into theinspection tool without having to put the wafer back into its cassetteor other container between the processes. The common power source mayinclude any suitable power source known in the art. The computersubsystem may be coupled to the metrology and inspection tools asdescribed further herein such that the computer subsystem can interactwith the metrology and inspection tools as described further herein. Theadditional module may be incorporated into the system in the same mannerdescribed above.

The hardware of the metrology tool may be disposed in a measurementchamber, that is separate from the inspection tool and additional moduleincluded in the system. The measurement chamber may be disposedlaterally or vertically proximate the inspection tool and the additionalmodule. For example, the system may be configured as a cluster ofmodules that may each be configured to perform different processes. Inaddition, the measurement chamber, the inspection tool, and theadditional module may be disposed laterally or vertically proximate loadchamber 414 of the system. The load chamber may be configured to supportmultiple wafers (or a lot) such as cassette 416 of wafers that are to beprocessed in the system. Wafer handler 408 may be configured to remove awafer from the load chamber prior to measurement and/or inspection andto place a measured and/or inspected wafer into the load chamber.Furthermore, the measurement chamber may be disposed in other locationsproximate the inspection subsystem such as anywhere where there issufficient space for the metrology tool hardware and anywhere a waferhandler may fit such that a wafer may be moved between the measurementchamber and the inspection tool. In this manner, wafer handler 408, astage (not shown), or another suitable mechanical device may beconfigured to move a wafer to and from the metrology and inspectiontools of the system.

The one or more computer subsystem(s) may be configured for determiningpositions of the at least two locations on the wafer with respect tolocations of the measurement points on the wafer. Determining positionsof the at least two locations on the wafer with respect to the locationsof the measurement points may include coordinate system matching. Inother words, for the correct overlay of metrology and other data, thecoordinate system and layout can be matched between the different data.The parameters that may be matched may include die size, die centerlocation (0,0 die), reticle (exposure field) sizes, and die/reticleorigin. In order to test a potential correct match of coordinatesystems, a specific measurement point that has been measured by themetrology tool can be imaged on the output acquisition subsystem so thatan x, y location reported by the output acquisition subsystem can becompared, correlated, and/or matched to the x, y location used in themetrology tool.

As described above, therefore, determining positions of the at least twolocations on the wafer with respect to the measurement point locationsmay include some kind of coordinate matching. That matching can beperformed in a number of different ways. For example, one or more commonreference points on a wafer that are measured or detected can beidentified and used to determine one or more offsets between thedifferent coordinates used and/or reported in. Those one or more offsetscan then be used to translate any one reported location from onecoordinate system to another. Once the locations under considerationhave been translated to the metrology coordinates or the measurementpoint locations have been translated to the wafer location coordinates,the locations under consideration with respect to the measurement pointlocations may be determined.

These relative locations may be determined in any suitable manner. Forexample, after coordinate system matching or translating, the at leasttwo locations that are the same as (or substantially the same as) themeasurement point locations may be identified. These at least twolocations may include any wafer locations that at least partiallyoverlap with the locations of the measurement points and/or only thewafer locations that are located entirely within a measurement pointlocation. In addition, after coordinate system matching or translation,the locations under consideration that are not substantially the same asany of the measurement point locations may be identified (which mayinclude most of the wafer locations). In some instances, the at leasttwo locations with respect to the measurement point locations may bedetermined simply in the common coordinate system generated bycoordinate system matching or translation. However, determining the atleast two locations with respect to the measurement point locations mayalso or alternatively include determining an offset or distance betweeneach of the at least two locations and the location(s) of the one ormore closest measurement points in the common coordinate systemgenerated by coordinate system matching or translation. Determining thepositions of the at least two locations with respect to the location(s)of the measurement points may therefore also include determining whichof the measurement point(s) is/are closest to the at least twolocations, and that information may also be stored with thecorresponding wafer locations. In general, therefore, different methodscan be used in the embodiments described herein to determine therelative locations between the at least two locations and themeasurement point locations in a common coordinate system.

The assigning includes, for the at least two locations having thepositions at the locations of the measurement points, assigning theacquired metrology data generated at the locations of the measurementpoints to the at least two locations based on which of the measurementpoints at which the at least two locations are positioned. For example,as described further above, determining the at least two locations withrespect to the measurement point locations may include detetmining ifany of the at least two locations overlap or are the same as themeasurement point locations in the same coordinate system. If any of themeasurement point locations overlaps or is the same as one of the atleast two locations, then the metrology tool has effectively performed ameasurement at the one location although that was not by design. In anycase, since a measurement has effectively been performed for the atleast two locations having positions that are the same as or overlapwith a measurement point location, the metrology data generated at themeasurement point locations may be assigned to those locations. In thismanner, if one of the at least two locations is positioned at a first ofthe measurement points, the one location may be assigned the metrologydata generated at the first of the measurement points; if another of theat least two locations is positioned at a second of the measurementpoints, the other location may be assigned the metrology data generatedat the second of the measurement points; and so on.

The assigning also includes, for the at least two locations having thepositions spaced from the locations of the measurement points,predicting the metrology data at the at least two locations from themetrology data generated at the measurement points and the positions ofthe at least two locations determined with respect to the locations ofthe measurement points. For example, the metrology data generated at themeasurement points may be used to predict the metrology data at the atleast two locations using one of the methods described herein. Since asignificant portion of the at least two locations will typically nothave the same positions as the measurement points on the wafer, thepredicting will be an important step in making the embodiments describedherein work properly.

In one embodiment, the predicting includes interpolation of the acquiredmetrology data from the measurement points to the positions of the atleast two locations determined with respect to the locations of themeasurement points. Interpolation can be generally defined in the art asthe prediction of values within a given data range. The interpolationused in the predicting step may include any suitable interpolationmethod known in the art. Examples of suitable interpolation methodsinclude, but are not limited to, linear interpolation, polynomialinterpolation, spline interpolation, non-linear interpolation,interpolation via a Gaussian process, multivariate interpolation,bilinear interpolation, and bicubic interpolation, all of which may beperformed in any suitable manner known in the art.

In another embodiment, the predicting includes extrapolation of theacquired metrology data from the measurement points to the positions ofthe at least two locations determined with respect to the locations ofthe measurement points. Extrapolation can be generally defined in theart as the prediction of data outside of a given data range. For ametrology to wafer location correlation, precise metrology data for allwafer locations on a wafer is preferable. Since the metrologymeasurements will not be performed at all wafer locations (e.g., due tothe time and expense involved in making the metrology measurements), themetrology data can be extrapolated to the wafer locations. The accuracyof the extrapolation depends on the density of the metrologymeasurements on the wafer and the model that is used for theextrapolation. The model that is used for extrapolation depends on themetrology use case (CD, film, overlay, etc.). There are differentmethods that can be used for extrapolation.

One such extrapolation method is contour plot based extrapolation. Forexample, once the metrology data has been acquired, a contour plot forthe data can be generated in any suitable manner known in the art. Oncea contour plot is available, a value of a wafer characteristic in themetrology data can be extracted for each point on the wafer in auser-defined grid size. This metrology value can then be applied towafer locations within that same grid. In this manner, metrology valuescan be assigned to each wafer location according to the value of thegrid in which the wafer location is located.

Another such extrapolation method is modeled based extrapolation, whichmay be particularly useful for overlay metrology data. Based on theinitial overlay measurements, a model can be generated in the 5DAnalyzer. The correct use of the model can be determined based on thesampling plan of the measurements and a model for the exposure tool usedto print the design on the wafer. Based on an available model, the datacan be exported for a user-defined number of measurement points acrossthe wafer. The 5D Analyzer can populate any defined point on the waferwith the modeled data from the actual measurements to get a relativelylarge number of modeled overlay data across the wafer. This data canthen be used like any other metrology data for the X and Y directions.The contour plot methodology described above can be applied for everywafer location.

For the modeled based methodology, the calculated model terms can beexported to the one or more computer subsystems. If the values for eachmodel term and each wafer measured as well as the model are exported tothe one or more computer subsystems, the computer subsystem(s) can thencalculate the overlay value for every location on the wafer. This methodwould reduce the required data transfer between the metrology tool (orthe 5D analyzer) and the one or more computer subsystems and would allowthe determination of the precise modeled overlay results for each waferlocation rather than an approximation with a grid as described above.Although the modeled based approach has been described above as beingused for overlay data, this approach can be used for any other metrologydata described herein because the 5D Analyzer has the capability tomodel any metrology data.

For the one of the locations on the wafer, selecting the SBG rule alsoincludes determining MC scores for SBG rules associated with thegeometric primitives in the FOV based on the determined distancesbetween the geometric primitives. In this manner, the method includesmodulating SBG complexities with design metrics and metrologymeasurements. Modulating SBG complexities with design metrics andmetrology measurements is important for a number of reasons. Forexample, different locations in a design having the same SBG geometricprimitives may have different as-designed distances between thegeometric primitives. In other words, first and second primitives in onelocation in a design spaced apart by a first dimension may also be foundin another location in the design spaced apart by a second dimensiondifferent than the first. These local design dimensions (and thedifferences between them at different locations in the design) can bemeasured or determined directly from the design intent Files. Differentinstances of the same SBG geometric primitives may also have differentcharacteristics on the wafer (at the same location in multiple instancesof the design (multiple dies or fields) printed on the wafer and/or atmultiple instances of the same SBG geometric primitives in a singleinstance of the design (a die or field) printed on the wafer). Forexample, the process with which the patterned features are formed on thewafer (e.g., lithography, etch, a combination thereof, otherprocess(es), etc.) may cause the dimensions and other characteristics(e.g., spatial relation of one feature relative to another, overlay,etc.) to vary from the as-designed dimensions and other characteristics.In one such example, a width of a space between two lines on a wafer canincrease from its as-designed width if the widths of the lines on eitherside of the space are smaller than their as-designed widths. In anothersuch example, a width of a space on a wafer can increase from itsas-designed width if the overlay shifts between two patterns on eitherside of the space change (the two patterns may be formed on the samelayer of the wafer in a multi-patterning process or the two patterns maybe formed on different layers of the wafer in multiple patterningprocesses). Therefore, such changes in the geometric primitivesincluding the distances between them due to the processes performed onthe wafer (or the wafer itself) cannot be estimated or determined fromthe design for the wafer. However, such changes in the geometricprimitives can be estimated or determined (e.g., via interpolation) bythe embodiments described herein.

The embodiments described herein address several issues with currentlyused methods and systems for SBG. For example, there are certaindrawbacks to SBG rules as they are currently specified which areaddressed by the embodiments described herein. One example of such adrawback is that while the pattern in a defect location and thesurrounding patterns that influence defect formation at the location canbe restricted by the sizes of short- and long-range windows,respectively, there is no further refinement of the criticality at alocation induced by a rule because currently used SBG methods andsystems do not consider the actual distances between the variousgeometric primitives specifying that rule. Another example of such adrawback is that SBG rules are based on identifying the existence ofprimitives, not their count. This blindness to counts also translates torules, e.g,, if the same rule hits multiple times at a crosshairlocation, due to is the presence of multiple primitives of the sametype, then SBG at present does not record these multiple hits, Anadditional example of such a drawback is that a similar althoughtechnically easier situation arises in the case of different ruleshitting at the same crosshair location.

Define the spatially modulated complexity function (smcf) C_(R)(X, Y) ofrule R∈R (where rule R is an element of a set of rules R) at designpoint (x, y) only if rule R∈R hits location (x, y); otherwise, it is 0.The value of the smcf C_(R)(x, y) point (x, y) is based on the distanceof the relevant, primitives in rule R from the crosshair position atthat point. These relevant primitives are the attacking primitivesdescribed further herein. Note that multiple versions of the same rulecan now hit the same (x,y) location, and because the values of theirsmcfs are different, it can lead to a high value of criticality at (x,y) equal to the maximum value of the smcfs.

As described further herein, we formulate the general principles thatwill allow defining the smcf for every rule. These general principleswill allow creating smcfs not only for all SBG rules (see, for example,U.S. Patent Application Publication No. 2017/0186151 published Jun. 29,2017 by Baneijee et al., which is incorporated by reference as if fullyset forth herein), but also any SBG rule that could be defined in thefuture. As also described further herein, we use the concept of the smcfof an SBG rule to provide a definition for its MC. The embodimentsdescribed herein may be further configured as described in theabove-referenced patent application publication.

When considering the directional force fields (DFFs) of geometricprimitives, imagine DFFs emanating from and surrounding geometricprimitives as the cause of a defect at the crosshair center of a defectlocation. For convex and concave corner primitives, the influence regionof these fields together with their associated directions are shown inFIG. 5 as examples 500 and 502, respectively. The geometric primitivesare indicated in this figure by the solid black areas, and the influenceregions of their DFFs with their associated directions are indicated bythe gray shaded areas and arrows, respectively, in this figure. Note theimplosive nature of a concavity in example 502 (i.e., the influenceregion is directed into the patterned feature). Example 504 in FIG. 5shows the influence region of the DFF associated with the line end(i.e., line tip) primitive. Example 506 in FIG. 5 shows the influenceregion of the DFF associated with a line (i.e., edge) primitive. Notethat while we have used black dots in defining rules for line primitivesin the above referenced patent application publication, the actualprimitive is a line segment as displayed in black in example 506. Thedot paradigm helps specify a rule by defining the proper primitives ineach quadr2ant—this is not required here.

Although it is not used in the current set of SBG rules, the jogprimitive is deemed to be an important geometric primitive and may beused in the SBG rules herein and in the future. For sake ofcompleteness, the influence region of the DFF associated with a jogprimitive is shown in example 508 in FIG. 5.

An SBG rule is specified by first fixing a point (x, y) in design spacewhere a defect is formed and/or where a wafer location for which thesteps described herein are being performed. The point (x, y) is thecenter of the crosshair around which quadrants bounded by influenceranges are formed, and the co-occurrence of the geometric primitives inthese quadrants specify a SBG rule. As defined herein, an attackingprimitive in an SBG rule is a geometric primitive in the influenceranges of that SBG rule that has a component of its DFF pointing to thecenter of the crosshair of that SBG rule, The importance of an attackingprimitive in a spatially modulated SBG rule lies in its distance withrespect to the crosshair center in the formulation of the smcf for arule.

The SBG rules are formulated so that the defect causing influences areeither along the horizontal or vertical directions, or both. Whether arule configuration has horizontal or vertical directions depends uponwhich of those directions gives higher criticality. As defined herein,the direction of highest criticality of a rule configuration is calledthe canonical direction of that rule configuration. From an observationof the SBG rules as described in the above-referenced patent applicationpublication, the canonical directionality of a rule for a givenconfiguration is easily determined. The Dihedral Group D8 (where aDihedral Group is a group of symmetries of an n-sided polygon inabstract algebra) acting on the directions of geometric primitivesallows each of the SBG rules to be evaluated invariant to the D8transformations. More importantly for the current situation, it allowsone to determine in a precipitation state, the canonical direction ofevery D8 configuration of each rule.

The smcf C_(R)(x, y) can be defined as follows. Place a crosshair atlocation (x, y). This corresponds to the crosshair center of the rule Raround which its influence windows are defined. It marks the putativedefect location or wafer location under consideration. Determine thecanonical direction of the configuration of rule R. Let d₁, . . . ,d_(N) be the non-negative distances of the N attacking primitives π₁, .. . , π_(N) for rule R configuration from the crosshair along itscanonical direction (horizontal or vertical). Then the smcf C_(R)(x, y)is defined as:

${C_{R}\left( {x,y} \right)}\overset{def}{=}{\frac{a_{1}}{\left( \frac{d_{1}}{D_{1}} \right)^{k_{1}}} + \frac{a_{2}}{\left( \frac{d_{2}}{D_{2}} \right)^{k_{2}}} + \ldots + \frac{a_{N}}{\left( \frac{d_{N}}{D_{N}} \right)^{k_{N}}}}$

where D_(i) is the nominal design distance corresponding to distanced_(i) as described further herein. The indices k_(i) are defined as:k_(i) is defined as being equal to 1 if π_(i) is a line primitive andk_(i) is defined as being equal to 2 if π_(i) is a point primitive. Thecomplexity coefficients a_(i)>0 are constants calculated from the userdefined complexities for a set of SBG rules.

Most importantly, the right-hand side of the above equation has thefollowing desirable properties. Any decrease in the distances d_(i) willincrease the value of the smcf C_(R)(x, y). Everything else being thesame, the value of the smcf C_(R′)(x, y) for a “sub-rule” R′ of R atlocation (z, y) will be lower than C_(R)(x, y).

The following concern arises from an inspection of the above equation:at what location (x, y) of the crosshair (along its canonical direction)is the value of complexity of the rule equal to the user definedcomplexity? If such a location is not specified, then the user providedcomplexity value C[R] for rule R is of no value because thespatially-modulated complexity value in the above equation isindependent of it. To address this issue, we first prescribe a nominalstate R0 of rule R that includes specifying the nominal design-dictatedconfigurations for R and specifying the normalizing position of thecrosshair for R. Once a nominal state R₀ of rule R is prescribed, itwill be used to determine the nominal design distance D_(i)corresponding to distance d_(i) of the i^(th) attacking primitive π_(i)of R.

For any design, the nominal minimum patterned feature (e.g., space andtrace) dimensions are specified. These dimensions are used to create thenominal long-range and short-range influence window sizes as describedin the above-referenced patent application publication. They can also beused to create the nominal design-dictated configuration for a rule. Forexample, if S and T represent the minimum space and trace dimensions,respectively, then the nominal configuration for SBG V5 Rule 16 is shownin FIG. 6, where the widths of the thin lines 602, 604, and 606 are allT, and the spaces between them all have widths S. Long-range influencewindow 600 and short-range influence window 608 have sizes determined asdescribed in the above-referenced patent application publication. Thelong and short range windows may be centered on origin 0 that is themid-point of a hit-line segment that has starting point 612 and endpoint 614 and that is in hit range 610, whose area is shown by the lightgray shaded area in FIG. 6.

Next, the normalizing position of the crosshair of a rule is alsoprescribed such that the nominal distance D_(i) corresponding todistance d_(i) of the attacking primitive π_(i) from the crosshair canbe calculated and applied to the above-equation. Observe that rule hitsoccur at contiguous locations on a line segment called a hit linesegment along the canonical direction of a rule, as the crosshair movesfrom the start to the end point along that direction. In FIG. 6, forexample, a hit line segment and its start and end points 612 and 614,respectively, for SBG V5 Rule 16 are shown. Note that depending on thelong-range and short-range window sizes, the same rule is hit at acontinuum of contiguous hit line segments orthogonal to the canonicaldirection—the hit range—hit range 610 shown in FIG. 6. As definedherein, the normalizing position of the crosshair for a rule is definedto be midway between the start and end positions of a hit line segmentfor that rule, Given space and trace dimensions S and T, the nominaldesign-dictated configuration for a configuration of rule R and thenormalizing position of its crosshair can be determined—this constitutesthe nominal state of rule R. The nominal state of rule R is used to findthe nominal design distance D_(i) corresponding to distance d_(i) of thei^(th) attacking primitive π_(i) of R.

Once the nominal state R₀ of a rule R has been determined as describedabove, the issue of incorporating the user-defined complexity can beresolved. As described herein, the complexity of a rule R at its nominalstate R₀ at the normalizing position of its crosshair is the userdefined complexity C_(R) of rule R. Since at the normalizing crosshairposition of the nominal state R₀ of rule R, the distances d_(i) areequal to their corresponding nominal design distances D_(i), combiningthis definition with the above equation yields: C_(R)=a₁+a₂+. . .+a_(N). Given a set of SBG rules, with a user-defined complexityassociated with each rule, one can determine the (best possible) valuesof the complexity coefficients a_(i).

The MC score of an SBG rule can be defined based on the aboveconsiderations and guided by the following observations. For a spatialdistribution of geometric primitives that trigger a rule, a singlecriticality value should suffice—having a contiguous set of values asgiven by its smcf for every location (x, y) would make it difficult toreturn a single score for the inspection or review sampling use cases.The smcf values at the extreme points of a hit line segment may beinfinitely large because the distance to one of the attacking primitivescould be 0. This phenomenon leads to even further inconvenience. Asdefined herein, the MC score of a rule R on a hit line segment is thevalue of its smcf at the normalizing position of its crosshair on thehit line segment. Therefore, although any SBG rule can have adistribution of MC scores determined for it, the embodiments describedherein may report and use a single MC score for any SBG rule.

For the one of the locations on the wafer, selecting the SBG rulefurther includes selecting one of the SBG rules for the one locationbased on the MC scores. In one embodiment, selecting one of the SBGrules includes identifying one of the SBG rules having a maximum of theMC scores as a most possible SBG rule for the one location and selectingthe most possible SBG rule for the one location. The phrase “mostpossible” has a strict mathematical definition akin to “most probable.”For example, for each location on the wafer for which the stepsdescribed herein are performed, a rule hit list may be generated thatincludes one or more rules that “hit” at that location. For each rule inthe hit list, an MC score can be determined as described herein. Themost possible rule for a location can then be determined as the SBG rulethat has the maximum of the MC scores determined for each rule in thehit list for that location.

The basis for complexity as used herein therefore corresponds to apossibility measure of complexity, where the complexity of the union isthe maximum of its constituents. This contrasts with the competingprobabilistic measure of complexity where the complexity of the union isthe sum of its disjoint constituents. The possibility measure ofcomplexity is more convenient for the embodiments described herein thanthe probabilistic measure for a number of reasons. For example, thepossibility measure of complexity is the natural formulation of theworkings of criticality, e.g., the most complex rule “wins.” In anotherexample, the additive probability theoretic measure must compute theintersecting (aka common) primitives and subtract off the complexity ofthat configuration, which introduces a further level of complication andconsequently computations. These complications are exacerbated whenmultiple hits beyond two are considered especially compared to thecomputations described herein. In an additional example, it is fareasier for humans to understand and debug the maximum measure results.In contrast, the additive measure results are often difficult tocomprehend because the effects of multiple hits and their intersectionsare inextricably confounded.

The one or more computer subsystems are also configured for sorting theat least two locations on the wafer based on the SBG rules selected forthe at least two locations. The sorting of the locations may varydepending on the application for which the steps of the embodiments areperformed. For example, in one embodiment, the sorting includesseparating the at least two locations into groups such that the SBG ruleselected for each of the locations in one of the groups are the same. Inthis manner, the sorting may include grouping locations by the SBG rulesselected for the locations (such that locations for which the same SBGrule is selected are in the same group and locations for which differentSBG rules are selected are in different groups). The sorting may also oralternatively include prioritizing the locations based on the SBG rulesselected for the locations. Prioritizing the locations may includeassigning a higher priority to locations for which an SBG rule having ahigher MC score was selected versus other locations assigned an SBG rulehaving a lower MC score. If the sorting includes the grouping asdescribed above, the groups may be prioritized in a similar manner.Results of the sorting may be used for one or more other steps describedherein.

In one embodiment, the at least two locations on the wafer includelocations of defects detected on the wafer by inspection, and thesorting includes sampling the defects detected at the least twolocations based on the SBG rules selected for the at least twolocations. In this manner, the embodiments described herein may be usedfor review (or other) sampling using metrical SBG. The locations of thedefects detected on the wafer by inspection may be determined oracquired as described herein (e.g., by or from an inspection tool). Thedefects may be sampled based on the SBG rules selected for the defectlocations in a number of different ways. For example, in one embodiment,the at least two locations on the wafer include locations of defectsdetected on the wafer by inspection, and the sorting includesprioritizing the defects for defect review based on the SBG rulesselected for the at least two locations. One advantage of theembodiments described herein is better prioritization of review samples.

In such an embodiment, the sampling may include comparing the MC scoresof the SBG rules selected for the locations of the defects and selectingthe defects having the highest. MC scores before other defects havinglower MC scores. In one such example, the defects may be sampled indescending order of the MC scores of their SBG rules. If defectlocations are sorted into groups based on their SBG rules, sampling thedefects may include determining different sampling schemes for differentgroups (e.g., sample number, percentage of group population, etc.) suchthat some groups (e.g., those assigned SBG rules having higher MCscores) are sampled more heavily than other groups (e.g., those assignedSBG rules having lower MC scores), such that each group is sampled atthe same rate, number, frequency, etc., such that defects having themost diverse characteristics in one or more of the groups are selected,such that defects are randomly selected from one or more of the groups,or in any other appropriate manner.

One embodiment of an initial flow of review sampling performed withmetrical SBG is shown in FIG. 7. The wafers that are sent for inspectionmay be a subset of the wafers on which metrology measurements have beenperformed at specific measurement points, and these metrologymeasurements have been recorded and can be accessed by wafer ID.Alternatively, metrology measurements have been performed on at leastsome wafers in the lot of wafers to be inspected. The metrologymeasurements may be performed as described further herein to therebygenerate any of the metrology data described herein. These measurementsand the estimated parameters of the process and metrology tools thatthey have gone through are then used to estimate the metrology data onthe wafers that are sent for inspection. For example, estimating themetrology data may include predicting metrology data at wafer locationsat which the measurements have not been performed, which may beperformed as described further herein.

All these details have been shown at a high level in FIG. 7 where wafer702 (e.g., a multi-patterned wafer) is shown to be sent to bothmetrology tool 700 and inspection tool 706. Metrology tool 700 mayinclude any of the metrology tools described herein, including CD andoverlay tools. Inspection tool 706 may include any of the inspectiontools described herein, including a BBP inspection tool.

The metrology tool generates metrology data 704 (e.g., CD and/or overlaymeasurements) at specific target sites on the wafer. A CD measurementtool may provide measurements on the local morphologies of patternedfeatures such as traces, and an overlay measurement tool may provide therigid affine transformation between multiple patterns and/or layers. Theinspection tool generates detected defect locations 708 on the wafer(s).Metrology data 704 and detected defect locations 708 are fed to dataanalysis 710, which may be performed by an interpolation module on the5D Analyzer to provide metrology estimates at defect locations 712(e.g., CD and overlay estimates at the defect locations).

Given the list of detected defect locations 708, computer subsystem(s)714, which may include any of the computer subsystem(s) described hereinand/or a Main UI, can request the design database, e.g., which may bestored on design based binning (DBB) server 716, to output all of thedesign polygons intersecting a field of view (FOV) of specified physicaldimensions, e,g., 2 μm or 4 μm, centered at every defect location (ortwo or more defect locations). The DBB server outputs design at defectlocations 718, which may include a set of design text tiles, one foreach defect location (or two or more defect locations) with the designinformation, e.g., multi-pattern related information. In this manner,the DBB server may output substantially small, discrete portions of thedesign, one corresponding to each of the defect locations underconsideration. The DBB server may have any suitable configuration knownin the art. The design information may also be acquired from any of thedesign data described herein and/or by any of the computer subsystem(s)or tools (e.g., an EDA tool) described herein.

Design rendering 720 may use the design at defect locations 718 torender (at a user-defined pixel size) image 722 showing all the designpolygons in the specified FOV with every polygon in the same patternbeing colored the same and polygons in different patterns being coloreddifferently. In other words, the rendered images may include someindicia (e.g., color) to indicate patterns that are formed on the waferin different lithography steps. Design rendering may be performed in anysuitable manner known in the art. The design rendering generates imagesthat illustrate the design intent (meaning the design data as it wasintended to be formed on the wafer rather than as it will be formed onthe wafer or imaged by a tool such as an inspection tool). In thismanner, rendered images 722 provide the design information portion ofthe data (e.g., geometric primitives information) on which the SBG rulesoperate. Thus, at the end of the initial flow shown in FIG. 7, there aretwo pieces of information, metrology estimates 712 and rendered images722 at every defect location under consideration.0

In one such embodiment, the sampling is performed by inputting the SBGrules selected for the at least two locations into a learning basedmodel. For example, the sampling can be performed by a machine learningbased algorithm, trained on metrical SBG features like MC scores, thatuses these features to create the sample. One advantage of theembodiments described herein is better features for machine learningbased sampling.

The computer subsystem(s), e.g., computer subsystem 36 and/or computersubsystem(s) 102, may be configured to execute one or more components(not shown) that include a learning based model. The learning basedmodel may be configured as a deep learning model. Generally speaking,“deep learning” (also known as deep structured learning, hierarchicallearning or deep machine learning) is a branch of machine learning basedon a set of algorithms that attempt to model high level abstractions indata. In a simple case, there may be two sets of neurons: ones thatreceive an input signal and ones that send an output signal. When theinput layer receives an input, it passes on a modified version of theinput to the next layer. In a deep learning based model, there are manylayers between the input and output (and the layers are not made ofneurons but it can help to think of it that way), allowing the algorithmto use multiple processing layers, composed of multiple linear andnon-linear transformations.

Deep learning is part of a broader family of machine learning methodsbased on learning representations of data. An observation (e.g., animage) can be represented in many ways such as a vector of intensityvalues per pixel, or in a more abstract way as a set of edges, regionsof particular shape, etc. Some representations are better than others atsimplifying the learning task (e.g., face recognition or facialexpression recognition). One of the promises of deep learning isreplacing handcrafted features with efficient algorithms forunsupervised or semi-supervised feature learning and hierarchicalfeature extraction.

Machine learning can be generally defined as a type of artificialintelligence (Al) that provides computers with the ability to learnwithout being explicitly programmed. Machine learning focuses on thedevelopment of computer programs that can teach themselves to grow andchange when exposed to new data. In other words, machine learning can bedefined as the subfield of computer science that “gives computers theability to learn without being explicitly programmed,” Machine learningexplores the study and construction of algorithms that can learn fromand make predictions on data—such algorithms overcome following strictlystatic program instructions by making data driven predictions ordecisions, through building a model from sample inputs.

The machine learning described herein may be further performed asdescribed in “Introduction to Statistical Machine Learning,” bySugiyama, Morgan Kaufmann, 2016, 534 pages; “Discriminative, Generative,and Imitative Learning,” Jehara, MIT Thesis, 2002, 212 pages; and“Principles of Data Mining (Adaptive Computation and Machine Learning),”Hand et aL, MIT Press, 2001, 578 pages; which are incorporated byreference as if fully set forth herein. The embodiments described hereinmay be further configured as described in these references.

One embodiment of a final flow, a continuation of the flow shown in FIG.7, of the review sampling performed with metrical SBG is shown in FIG.8. It starts with the two pieces of information generated in the initialflow—metrology estimates 712 and rendered images 722 at every defectlocation under consideration, and generates the MC scores for everydefect location under consideration.

Rendered images 722 are input in FIG. 8 to shape primitives extraction800 that processes the rendered images to generate output 802, the setof SBG primitives at every defect location FOV. Passing each one ofthese primitive images through SBG rule extraction engine 804 producesrule hit list 806 that records all the rules hit at every defectlocation being considered.

Given rule hit list 806 and primitive image list 802, the MetricalComplexity scorer 808 computes the various distances in the designintent between the various geometric primitives involved in calculatingthe MC scores for each rule hit at every defect location underconsideration. Furthermore, using the corresponding metrology estimatelist 712, the Metrical Complexity scorer 808 determines the shifts ofthese geometric primitives in order to refine the distances between theprimitives at every defect location. This step may be performed asdescribed further herein. This refinement provides more accurate MCscores for each rule hit than the ones obtained from design intent only.Finally, the Metrical Complexity scorer 808 computes the MC scores foreach rule hit at every defect location under consideration and the mostpossible rule with the maximum MC score at every defect location underconsideration, and outputs this is information in list 810. These stepsmay be performed as described further herein.

The MC scores along with the rule information in list 810 is then usedby sampling 812 to create a prioritized sample of defect locations,e.g., sorted in descending priority order for review. This samplingcould be performed with an out-of-the-box algorithm that uses theinformation in list 810, especially the MC scores, to create theprioritized sample, much like the round-robin scheme described in U.S.Patent Application Publication No. 2019/0072858 by Saraswatula et al.published Mar. 7, 2019, which is incorporated by reference as if fullyset forth herein. The embodiments described herein may be furtherconfigured as described in this patent application. The sampling canalso be performed with a machine learning based algorithm, trained onmetrical SBG features like MC scores, and using these features to createthe prioritized sample. This sample is then subsequently sent to tool814, which may be a SEM review tool or any other suitable defect reviewtool, that visits and captures high resolution images 816, e.g., SEMimages, at the defect locations specified in the sample. The sample ofdefect locations may also be used by one or more other tools for one ormore other processes (e.g., metrology, defect repair, etc.).

In some embodiments, the at least two locations include SBG rule hitlocations, and the one or more computer subsystems are configured foridentifying the SBG rule hit locations by searching the design for thewafer for geometric primitives associated with one or more of the SBGrules. This step may be performed as part of a micro care area (MCA)generation use case for metrical SBG.

FIG. 9 depicts one embodiment of steps that may be performed by theembodiments described herein for MCA generation using metrical SBG. Inthis case, the desig0n data for water lot 900 residing in server 902 isloaded by design database loader 904 into design data 906, which mayhave any suitable format known in the art such as the RDF design dataformat, a proprietary design data format available commercially fromKLA-Tencor, or any other format that protects both the design user anddesign owner from sensitive information leakages. The design data isthen operated on by SBG engine 908 that extracts shape primitives usingshape primitives extraction 800 shown in FIG. 8, finds locations whereeach rule is triggered using rule extraction engine 804 in FIG. 8, andgenerates SBG rule hit locations 916. This design analysis procedure canbe a one-time setup step on any design intent data and may be performedas described further herein.

Metrology measurements on wafer lot 900 are carried out by metrologytool 910, e.g., a CD and/or overlay metrology tool. The metrology toolgenerates metrology data 912, e.g., CD and/or overlay measurements, atspecific measurement points on a sample (one or more) of wafers in lot900. If the metrology tool is a CD metrology tool, the metrology toolmay provide measurements on the local morphologies of traces or otherpatterned features on the water sample. If the metrology tool is anoverlay metrology tool, the metrology tool may provide the rigid affinetransformations between multiple patterns and/or layers on the wafersample. These results are fed into data analysis 914, which may be aninterpolation algorithm module in the 5D Analyzer, to provide metrologyestimates for wafers in lot 918, which may include CD and/or overlayestimates for every location under consideration on the wafers in lot900. Alternatively, metrology estimates 918 can be determined at onlySBG rule hit locations 916 triggered by the SBG engine 908, thisdependency being shown in FIG. 9 by the dotted light joining 916 to 918.Hit locations 916 and metrology estimates 918 are fed into MetricalComplexity Scorer 920, which generates the MC scores and the mostpossible rule triggered at all (or at least some of) hit locations 916on each wafer in lot 900. These steps may be performed as describedfurther herein.

In one embodiment, the one or more computer subsystems are configuredfor generating care areas for the at least two locations based on theSBG rules selected for the at least two locations. As described furtherherein, “care areas” as they are commonly referred to in the art areareas on a specimen that are of interest for inspection purposes.Sometimes, care areas are used to differentiate between areas on thespecimen that are inspected from areas on the specimen that are notinspected in an inspection process. In addition, care areas aresometimes used to differentiate between areas on the specimen that areto be inspected with one or more different parameters. For example, if afirst area of a specimen is more critical than a second area on thespecimen, the first area may be inspected with a higher sensitivity thanthe second area so that defects are detected in the first area with ahigher sensitivity. Other parameters of an inspection process can bealtered from care area to care area in a similar manner. The care areasmay also be “micro care areas” in that their dimensions may besubstantially small, e.g., on the order of a few (less than 10) pixels.

In some embodiments, a care area or care area information may begenerated for each location for which the steps were performed (e.g.,every SBG rule hit location). However, in some instances, the results ofthe embodiments described herein may indicate that a care area is notneeded for every SBG rule hit location. For example, in some instances,a cut off may be applied to the MC scores of the SBG rules selected foreach of the wafer locations, and care areas may not be generated forlocations whose selected SBG rules do not have an MC score above the cutoff. Generating care areas for only locations whose selected SBG ruleshave an MC score above a certain threshold. may be advantageous becauseeven if two locations on the wafer have the same geometric primitiveswith the same as-designed distances between them, the metrology data forthe two locations may be different enough that one of the locations hasan MC score that is high enough to warrant a care area while another ofthe locations has an MC score that is not high enough to justify a carearea. Therefore, generating the care areas may include determining,based on the MC scores of the SBG rules selected for different waferlocations, which wafer locations for which care areas will be generatedand which wafer locations for which care areas will not be generated.

Generating the care areas may also include determining one or more otherparameters with which the care areas are to be processed. For example,generating the care areas may include assigning inspection parameters tocare areas for wafer locations for which the selected SBG rules havehigher MC scores that result in these care areas being inspected withhigher sensitivity than care areas for wafer locations for which theselected SBG rules have lower MC scores. Any other parameter of anyprocess (e.g,, a metrology process) may be determined for the care areasin the care area generating step. The results of the care areagenerating step may be output to a recipe (e.g., an inspection recipe)such that the care areas can be used in the process performed with thatrecipe. In addition, the care area generating may include generating anyother suitable information for the care areas (e.g., location,dimension, care area ID, care area group ID, parameter(s) to be used fordifferent care areas, etc.).

In a further embodiment, the one or more computer subsystems areconfigured for prioritizing care areas for the at least two locationsbased on the SBG rules selected for the at least two locations. Oneadvantage of the embodiments described herein is better prioritizationof MCAs for inspection. The care areas (or MCAs) can be prioritized asdescribed further herein (e.g., based on the MC scores associated withthe SBG rules selected for the care areas).

In another embodiment, the one or more computer subsystems areconfigured for generating a context map for the wafer in which the SBGrules selected for the at least two locations are associated with the atleast two locations, and the system includes an inspection toolconfigured to perform inspection of the wafer using one or moreinspection parameters defined based on the context map. For example, hitlocations 916 shown in FIG. 9 on each wafer (or at least one wafer) maybe converted to context map 922 for wafers in the lot, and the contextmap may have a KLA-Tencor proprietary format called a. run-time contextmap (RTCM) or any other suitable format known in the art. The contextmap can be used to generate (or modify) inspection recipe 924 for wafersin the lot, which can be used by inspector 926 to assign differentsensitivities to different regions on the wafer based on the rulestriggered (e.g., the SBG rule hit locations), the computed MC scores(e.g., determined by the MC scorer), and the most possible ruleidentified (the SBG rules selected for the at least two waferlocations). Such an inspection will advantageously be a more hotspotsensitive and less nuisance prone inspection of the wafers in lot 900(and possibly other lots of wafers of the same type).

The context map may therefore not include care areas generated asdescribed herein. Instead, based on the information included in thecontext map, the inspection tool can, using the inspection recipe,determine which areas on the wafer are to be inspected and with whatparameters. The areas to be inspected and the parameters to be used forthe inspection may be determined as described further herein (e.g.,based on the MC scores determined for the SBG rules selected for eachwafer location for which the steps described herein were performed).While such a context map may be most suitable for use in an inspectionprocess, the context map may also be used for any other processperformed on the wafer such as a defect review process, a metrologyprocess, a defect repair process, and the like. The parameters of suchprocesses may be varied based on the context map as described furtherherein.

An of the methods described herein may include storing results of one ormore steps of the method embodiments in a computer-readable storagemedium. The results may include any of the results described herein andmay be stored in any manner known in the art. The storage medium mayinclude any storage medium described herein or any other suitablestorage medium known in the art. After the results have been stored, theresults can be accessed in the storage medium and used by any of themethod or system embodiments described herein, formatted for display toa user, used by another software module, method, or system, etc. toperform one or more functions for the wafer or another wafer of the sametype. Such functions include, but are not limited to, performing adefect review (or other) process on the defects sampled by the computersubsystem(s), reviewing defects on the wafer based on results ofprioritizing the defects performed by the computer subsystem(s),inspecting a wafer based on results of generating and/or prioritizingcare areas performed by the computer subsystem(s), and performinginspection of a wafer using a context map generated by the computersubsystem(s).

The embodiments described herein can also be combined with the systemsand methods described in U.S. Patent Application Publication No.2019/0067060 by Plihal et al. published Feb. 28, 2019, which isincorporated by reference as if fully set forth herein. The embodimentsdescribed herein may be further configured as described in this patentapplication.

Each of the embodiments of the system may be further configuredaccording to any other embodiment(s) described herein.

Another embodiment relates to a computer-implemented method for shapemetric shape based sorting of wafer locations. The method includes theselecting the STSG rules and sorting steps described above.

Each of the steps of the method may be performed as described furtherherein. The method may also include any other step(s) that can beperformed by the output acquisition subsystem, computer subsystem(s),and/or system(s) described herein. The steps of the method are performedby one or more computer subsystems, which may be configured according toany of the embodiments described herein. In addition, the methoddescribed above may be performed by any of the system embodimentsdescribed herein.

An additional embodiment relates to a non-transitory computer-readablemedium storing program instructions executable on a computer system forperforming a computer-implemented method for shape metric based sortingof wafer locations. One such embodiment is shown in FIG. 10. Inparticular, as shown in FIG. 10, non-transitory computer-readable medium1000 includes program instructions 1002 executable on computer system1004. The computer-implemented method may include any step(s) of anymethod(s) described herein.

Program instructions 1002 implementing methods such as those describedherein may be stored on computer-readable medium 1000. Thecomputer-readable medium may be a storage medium such as a magnetic oroptical disk, a magnetic tape, or any other suitable non-transitorycomputer-readable medium known in the art.

The program instructions may be implemented in any of various ways,including procedure-based techniques, component-based techniques, and/orobject-oriented techniques, among others. For example, the programinstructions may be implemented using ActiveX controls, C++ objects,JavaBeans, Microsoft Foundation Classes (“MFC”), SSE (Streaming SIMVExtension) or other technologies or methodologies, as desired.

Computer system 1004 may be configured according to any of theembodiments described herein.

Further modifications and alternative embodiments of various aspects ofthe invention will be apparent to those skilled in the art in view ofthis description. For example, methods and systems for shape metricbased scoring of wafer locations are provided. Accordingly, thisdescription is to be construed as illustrative only and is for thepurpose of teaching those skilled in the art the general manner ofcarrying out the invention. It is to be understood that the forms of theinvention shown and described herein are to be taken as the presentlypreferred embodiments. Elements and materials may be substituted forthose illustrated and described herein, parts and processes may bereversed, and certain features of the invention may be utilizedindependently, all as would be apparent to one skilled in the art afterhaving the benefit of this description of the invention. Changes may bemade in the elements described herein without departing from the spiritand scope of the invention as described in the following claims.

What is claimed is:
 1. A system configured for shape metric basedsorting of wafer locations, comprising: one or more computer subsystemsconfigured for: selecting shape based grouping rules for at least twolocations on a wafer, wherein for one of the locations on the wafer,selecting the shape based grouping rule comprises: determining distancesbetween geometric primitives in a field of view centered on the onelocation by modifying distances between the geometric primitives in adesign for the wafer with metrology data for the one location on thewafer; determining metrical complexity scores for shape based groupingrules associated with the geometric primitives in the field of viewbased on the determined distances between the geometric primitives; andselecting one of the shape based grouping rules for the one locationbased on the metrical complexity scores; and sorting the at least twolocations on the wafer based on the shape based grouping rules selectedfor the at least two locations.
 2. The system of claim 1, wherein saidselecting one of the shape based grouping rules comprises identifyingone of the shape based grouping rules having a maximum of the metrologycomplexity scores as a most possible shape based grouping rule for theone location and selecting the most possible shape based grouping rulefor the one location.
 3. The system of claim 1, wherein the at least twolocations on the wafer comprise locations of defects detected on thewafer by inspection.
 4. The system of claim 1, wherein the at least twolocations on the wafer comprise locations of defects detected on thewafer by inspection, and wherein said sorting comprises sampling thedefects detected at the at least two locations based on the shape basedgrouping rules selected for the at least two locations.
 5. The system ofclaim 4, wherein the sampling is performed by inputting the shape basedgrouping rules selected for the at least two locations into a learningbased model.
 6. The system of claim 1, wherein the at least twolocations on the wafer comprise locations of defects detected on thewafer by inspection, and wherein said sorting comprises prioritizing thedefects for defect review based on the shape based grouping rulesselected for the at least two locations.
 7. The system of claim 1,wherein the at least two locations comprise shape based grouping rulehit locations.
 8. The system of claim 7, wherein the one or morecomputer subsystems are further configured for identifying the shapebased grouping rule hit locations by searching the design for the waferfor geometric primitives associated with one or more of the shape basedgrouping rules.
 9. The system of claim 1, wherein said sorting comprisesseparating the at least two locations into groups such that the shapebased grouping rule selected for each of the locations in one of thegroups are the same.
 10. The system of claim 1, wherein the one or morecomputer subsystems are further configured for generating care areas forthe at least two locations based on the shape based grouping rulesselected for the at least two locations.
 11. The system of claim 1,wherein the one or more computer subsystems are further configured forprioritizing care areas for the at least two locations based on theshape based grouping rules selected for the at least two locations. 12.The system of claim 1, wherein the one or more computer subsystems arefurther configured for generating a context map for the wafer in whichthe shape based grouping rules selected for the at least two locationsare associated with the at least two locations, and wherein the systemfurther comprises an inspection tool configured to perform inspection ofthe wafer using one or more inspection parameters defined based on thecontext map.
 13. The system of claim 1, wherein the one or more computersubsystems are further configured for acquiring the metrology data forthe wafer from a metrology tool that performs measurements on the waferat an array of measurement points on the wafer and assigning themetrology data to the at least two locations on the wafer based onpositions of the at least two locations on the wafer determined withrespect to locations of the measurement points on the wafer.
 14. Thesystem of claim 13, wherein a density of the measurement points on thewafer is less than a density of inspection points on the wafer at whichoutput is generated by a detector of an inspection tool duringinspection of the wafer.
 15. The system of claim 13, wherein saidassigning comprises: for the at least two locations having the positionsat the locations of the measurement points, assigning the acquiredmetrology data generated at the locations of the measurement points tothe at least two locations based on which of the measurement points atwhich the at least two locations are positioned; and for the at leasttwo locations having the positions spaced from the locations of themeasurement points, predicting the metrology data at the at least twolocations from the metrology data generated at the measurement pointsand the positions of the at least two locations determined with respectto the locations of the measurement points.
 16. The system of claim 15,wherein said predicting comprises interpolation of the acquiredmetrology data from the measurement points to the positions of the atleast two locations determined with respect to the locations of themeasurement points.
 17. The system of claim 15, wherein said predictingcomprises extrapolation of the acquired metrology data from themeasurement points to the positions of the at least two locationsdetermined with respect to the locations of the measurement points. 18.The system of claim 13, wherein the metrology tool generates themetrology data for the wafer prior to inspection of the wafer.
 19. Thesystem of claim 13, wherein the measurement points are determined priorto inspection of the wafer and independently of defects detected on thewafer.
 20. The system of claim 13, wherein at least some values of themetrology data generated by the metrology tool are below a resolutionlimit of an inspection tool that performs inspection of the wafer. 21.The system of claim 13, wherein the metrology data comprises one or moreof film thickness, patterned structure profile, critical dimension, lineedge roughness, line width roughness, and overlay measurements.
 22. Thesystem of claim 13, wherein the metrology data comprises one or more oflithography focus metrology and scanner leveling data.
 23. The system ofclaim 13, wherein the metrology data comprises measurements of acharacteristic of the wafer known to correlate with patterning defects,24. The system of claim 1, further comprising an output acquisitionsubsystem comprising at least an energy source and a detector, whereinthe energy source is configured to generate energy that is directed tothe wafer, wherein the detector is configured to detect energy from thewafer and to generate output responsive to the detected energy, andwherein the one or more computer subsystems are further configured todetermine information for the at least two locations based on theoutput.
 25. The system of claim 24, wherein the output acquisitionsubsystem is configured as an inspection subsystem.
 26. The system ofclaim 24, wherein the output acquisition subsystem is configured as ametrology subsystem.
 27. The system of claim 24, wherein the outputacquisition subsystem is configured as a defect review subsystem. 28.The system of claim 24, wherein the energy directed to the wafercomprises light, and wherein the energy detected from the wafercomprises light.
 29. The system of claim 24, wherein the energy directedto the wafer comprises electrons, and wherein the energy detected fromthe wafer comprises electrons.
 30. A non-transitory computer-readablemedium, storing program instructions executable on a computer system forperforming a computer-implemented method for shape metric based sortingof wafer locations, wherein the computer-implemented method comprises:selecting shape based grouping rules for at least two locations on awafer, wherein for one of the locations on the wafer, selecting theshape based grouping rule comprises: determining distances betweengeometric primitives in a field of view centered on the one location bymodifying distances between the geometric primitives in a design for thewafer with metrology data for the one location on the wafer; determiningmetrical complexity scores for shape based grouping rules associatedwith the geometric primitives in the field of view based on thedetermined distances between the geometric primitives; and selecting oneof the shape based grouping rules for the one location based on themetrical complexity scores; and sorting the at least two locations onthe wafer based on the shape based grouping rules selected for the atleast two locations.
 31. A computer-implemented method for shape metricbased sorting of wafer locations, comprising: selecting shape basedgrouping rules for at least two locations on a wafer, wherein for one ofthe locations on the wafer, selecting the shape based grouping rulecomprises: determining distances between geometric primitives in a fieldof view centered on the one location by modifying distances between thegeometric primitives in a design for the wafer with etrology data forthe one location on the wafer; determining metrical complexity scoresfor shape based grouping rules associated with the geometric primitivesin the field of view based on the determined distances between thegeometric primitives; and selecting one of the shape based groupingrules for the one location based on the metrical complexity scores; andsorting the at least two locations on the wafer based on the shape basedgrouping rules selected for the at least two locations, whereinselecting the shape based grouping rules and the sorting steps areperformed by one or more computer subsystems.